جستجوی مقالات مرتبط با کلیدواژه « discrete wavelet transform » در نشریات گروه « فنی و مهندسی »

The use of raw radiography results in lung disease identification has not acceptable performance. Machine learning can help identify diseases more accurately. Extensive studies were performed in classical and deep learning-based disease identification, but these methods do not have acceptable accuracy and efficiency or require high learning data. In this paper, a new method is presented for automatic interstitial lung disease identification on radiography images to address these challenges. In the first step, patient information is removed from the images; the remaining pixels are standardized for more precise processing. In the second step, the reliability of the proposed method is improved by Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of final features is reduced with Locality Sensitive Discriminant Analysis. The processed images are divided into learning and test categories in the third step to create different models using learning data. Finally, the best model is selected using test data. Simulation results on the NIH dataset show that the decision tree provides the most accurate model by improving the harmonic mean of sensitivity and accuracy by up to 1.09times compared to similar approaches.

Steganography is the science and art of hiding the existence of communication. In steganography, by hiding information in a digital media, the existence of communication remains hidden from the enemy’s view. The idea of spatial-based adaptive methods is to embed more in the edge areas of the image. In these methods, areas of the image that have more changes are prioritized for embedding. On the other hand, wavelet-based methods perform embedding in high frequency subbands to match the human vision system. The proposed idea in this paper is embedding with higher priority in areas of high frequency subbands resulting from wavelet transform that have many changes. First, according to the length of the data, a threshold value is determined, based on which suitable embedding areas are identified in each high frequency subband, and then the embedding process is performed in them. This process is such that the receiver can extract the data completely by repeating it. The implementation results show that in the proposed method, the use of Integer Wavelet Transform (IWT) is more successful than Discrete Wavelet Transform (DWT). The quality of the resulted stego image is higher and its security is more than the other wavelet-based methods.

Bridges are important components of the road networks and are classified as high importance from the structural safety standpoint. Therefore, maintaining their stability and improving their performance are important. The issue becomes more important considering that most of the existing bridges in the national transportation system are old, which can lead to their failure when subjected to dynamic loads caused by earthquakes or other sources. One of the conventional methods for health monitoring of structures and detecting the damage state is structural signal processing in the time-frequency domain using wavelet transformation. Previous studies proved the efficiency of this method. In this study, to benefit from the capabilities of this approach, one of the Jinnah grade-separation bridges in Tehran with a segmental deck under different damage scenarios has been studied. For this purpose, the difference in the displacement response between the intact and damaged bridge deck under the effect of the considered load is used as the input signal of wavelet transformation. The results show that the proposed method can accurately identify the damage location using one-time sampling and a minimum number of sensors.

There are different methods to determine in situ stress. In this regard, direct measurement methods are the best and most accurate in situ stress measurement methods. However, these methods are very time-consuming and costly. Therefore, nowadays laboratory and indirect rock core-based methods for estimating in situ stress have been taken into consideration. One of the laboratory methods is the acoustic emission method based on Kaiser effect. In this method, determining the Kaiser effect point is usually done by the parameters of acoustic emission signals, which in some cases, determining the Kaiser effect point by parametric method are ambiguous and do not have sufficient clarity. Signal processing based on wavelet transform is a powerful tool to process acoustic emission signals, which has been used in several papers.

Methodology and Approaches:

In this study some Phyllite samples were used to be loaded under indirect tensile loading (Brazilian test). The acoustic emission test consisted of two cycles. In the first cycle, the samples were preloaded to a predetermined level, and in the second cycle, they were reloaded until reaching the failure. Before using the discrete wavelet transform, the appropriate mother wavelet was selected. Then, the signals obtained from the acoustic emission test were processed using wavelet transform. The maximum approximation coefficient parameter was used as a suitable feature for the analysis. Using K-means method, the obtained data are clustered in five clusters. Then, the highest density of data in the fifth cluster was considered as the occurrence point of the Kaiser effect.

The results show that according to the parameters of correlation coefficient and noise to signal ratio and the type of acoustic signals the mother wave db6 was suitable for analysis. Among the parameters that can be used for analysis, the maximum approximation coefficient parameter is also selected as a suitable feature for analysis. Also, the results of the discrete wavelet transform method were in good agreement with the results of the parametric method, so that the occurrence times of the Kaiser effect obtained from the two methods are mutually acceptable.

Stress is one of the problems of today's societies that is rapidly developing in all aspects of life and can be the root of many psychological and physical problems. Stress occurs when a person exerts more pressure than he or she is accustomed to. Since stress is a factor that can severely affect the performance of individuals. Therefore, the diagnosis and evaluation of stress in professionals has become an important category. Early detection can prevent serious harm to people. One of the ways to diagnose stress is to use vital signals. In this study, vital nerve and muscle signal or electromyography was used to diagnose stress. For this purpose, the biceps muscle signal of 5 people in two positions before stress and during stress are analyzed by using mathematical parameters according to the values of the criteria and their evaluation in different people. The proposed method shows the signal correlation rate of 0.0970.

Dissolved gases in transformer oil indicate the occurrence of a fault in the transformer. Many standards use the ratio of dissolved gases in transformer oil to detect faults in transformers. These traditional methods cannot, however, be used in cases in which the transformer needs to be immediately removed from service in case of a serious error such as electric arcs to prevent the error from spreading. For this purpose, this paper uses signal processing methods that analyze the signal online. The paper assumes that CO and CO2 dissolved gases in transformer oil are measured online by a sensor and then some signal processing methods are applied to the measurement data. These methods include Fast Fourier Transform, Discrete Wavelet Transform, Hilbert Transform, Gabor Transform, the combination of discrete wavelet and Hilbert transform, the combination of discrete wavelet and Gabor transform, and spectrogram. These methods are continuously applied to these two gases that are soluble in transformer oil to determine their variations in the transformer oil at a certain time or a certain frequency. The gases are also modified and then the performance of each of the processing methods mentioned in these changes is investigated. Fault detection reference is the CIGER 761-2019 standard. The purpose of this paper is to find out the samples of gases change in a frequency interval or timeframe together irrespective of the faults in the transformer and changes in the volume of dissolved gases in transformer oil, analyze the signal processing methods, and detect the type of fault using CIGRE 761-2019 standard. The fast Fourier transform method analyzes signal power by frequency. The discrete wavelet transform method extracts high-frequency components of gases and detects faults based on the largest components. The Hilbert transform method converts the signal into two real and imaginary parts. Then, it uses the imaginary part that represents the signal phase to detect faults. The Gabor transform method extracts instantaneous frequencies in the time-frequency plane and uses this method to detect faults. In the methods that combine discrete wavelet transform and Hilbert or Gabor transform, high-frequency components are extracted by discrete wavelet transform, and then Hilbert or Gabor transform methods are applied. The spectrogram method also indicates the size of the short-time Fourier transform, which is used to analyze the signal. These signal processing methods are compared in several features, and the discrete wavelet transform method is introduced as the best method for fault detection.

Identification of infrastructure damage in the early stages is one of the most fundamental requirements through the maintenance process. In order to identify damages in the process of Structural Health Monitoring, numerous methods have been developed, including Neural Network (NN) Damage Identification Techniques, Time Series Damage Identification Techniques, Frequency Response Damage Identification Techniques, Force Spectrum Density Damage Identification Techniques, and Wavelet Damage Identification Techniques. The conventional structural health monitoring procedure depends on a comparison of undamaged primary structural data to the affected structural data. Comparative algorithms to indicate the location of damage based on the structural data has a significant effect on the structural health monitoring method performance. Therefore, a damage detection algorithm is the most important step in identifying damage in the structural health monitoring procedure. In this paper, a damage identification algorithm based on the relative wavelet entropy method is proposed for the structure. Using the definition of the discrete wavelet transform with wavelet entropy form the base of the proposed algorithm for damage detection to support the method efficiency. The proposed method is capable of identifying several sites of damage and is used as an effective method compared to other methods of damage detection.

 In the handling of medical images, the main priority is to secure protection for the patient’s documents against any act of tampering by unauthorized persons. Thus, the main concern of the existing electronic medical system is to develop some standard solution to preserve the authenticity and integrity of the content of medical images.Accordingly, digital image watermarking has many applications, one of its most important applications in Protection of medical images, engrave names, Signatures and Patient data on pictures, Videos etc. that are not so clear.There are several ways to digital image watermarking, but one of the most widely used methods to achieve robust watermarking to all kinds of attacks using the combination dwt and svd.We used in this research 2 level of haar wavelet transform on the host image and one level of single value decomposition on its low frequency subset and combined with a watermark coefficient and another level of singular value decomposition to embed the watermark and increase the watermark robustness in a way that when extracting a watermark can be done semi-blindly. With this method, we were able to improve the average peak signal to noise ratio of 55 and 7% improvement for the invisibility of the watermark and also the average correlation coefficient of 0.97 and 34% improvement to increase the resistance of the watermark to various attacks.

Any heart activity disorder may lead an irregularity in is rhythm, or cardiac arrhythmia. An ECG signal is one of the major tools for classifying different types of cardiac arrhythmias. ECG signals usually contain various noises. To have a better signal processing, it is essential to remove noises in a way that a signal structure never become subject to distortion. After the step of noise removal, selection of an appropriate method is of paramount importance for feature extraction. Optimal features can be selected to improve efficiency and reduce calculations.

This article used the ensemble empirical mode decomposition (EEMD) in which any intrinsic mode function (IMF) contains only a single frequency component for noise removal. The noise removal operation with the least distortion is possible using an appropriate windowing on a QRS complex containing sum of the first three IMFs. Later, the remaining noises can be removed using discrete wavelet transform (DWT). The results of using the EEMD-DWT combined method were compared with EMD and DWT combination. After the noise removal step, feature extraction was performed through a wavelet packet decomposition. It is capable of signal decomposition at all frequencies. Multiple objective binary particle swarm optimization (MOBPSO) method was used to select optimal features and the effect of this method on the results was examined. Finally, the back propagation neural network (BPNN) and a support vector machine based on particle swarm optimization were used for classification.

This article used 17 signals received from the MIT-BIH database. The acquired data belong to 6 different types of classes. After pre-processing, feature extraction, feature selection, and classification on the input data, it is observed that the proposed technique of EEMD-DWT is an appropriate method for noise removal and MOBPSO is a suitable method for the selection of best features. The BPNN classifier managed to classify cardiac arrhythmias with a higher accuracy and the values for accuracy, sensitivity, specificity, and positive predictive value were 99.12%, 97.08%, 99.38%, and 97.12%, respectively.