Health monitoring allows small failures and damages to be identified and fixed before they turn into major and irreparable damages, to prevent loss of life and to make it possible to reinforce or improve it at the lowest cost. Currently, in the field of civil engineering, the health monitoring of structures is done in sensitive structures. One of the parts of the structure that may suffer initial damage before loading and during implementation due to difficulty in implementation is Concrete Filled Tube (CFT)columns.  One of the most likely damages in CFT columns is interface debonding damage. This damage causes the column to become weak and not benefit from the characteristics of steel and concrete together. Accordingly, in the present study, this damage and its severity in seismic (dynamic) parameters have been investigated. The results of the study show that damage causes changes in the mode shape of the structure, and it has caused a 6.38% reduction in the frequency in the first (main) mode of the structure. Also, the damping of the damaged specimen is reduced by approximately 12% compared to the healthy specimen. On the other hand, the results show that the severity of damage is very effective in changing seismic parameters. So that by doubling the damage area, the frequency decreased by approximately 0.35% and reached from 873.27 Hz to 20.870.20 Hz, but the mode shape of damage did not affect the frequency

Introduction & Background: Breast cancer is known as one of the major causes of mortality among women. Early detection has been shown as the most effective strategy to reduce breast cancer mortality. Digital mammography is a new technology that uses high resolution and contrast devices. The aim of this research is to apply wavelet transform (WT) to enhance image resolution for better lesion detection. Patients & Methods: Since digital mammography is not available from local clinics, we used mammon-grams from Mammography Image Analysis Society (MIAS) database in our research. At first, median filter was used several times to reduce noise in mammogram images. Then the mammogram histogram was equalized and WT was used to enhance the imp-ages. The enhancement technique used, was local and adaptive for each of the sub-band images created after applying the WT multiresolution analysis. Afterwards an adaptive threshold according to the highest gray level of the image was used to save them, so that no probable micro calcification in the images would be lost. As a result, the contrast of images and the differ-emcees between benign and malignant abnormalities are highlighted for better visualization. Results: This procedure was implemented on 85 imp-ages from MIAS. In 78% of cases, the processed images were better than before. Conclusion: By using digital image processing tech-inquest on digital mammography, it is possible to in-crease contrast, to zoom on desired regions, to measure lesions, and to enhance images for better inter-predations.

Identification of damping parameter is usually more complicated and unreliable comparing to mass or stiffness identification in structural dynamics. There are many factors such as intermolecular friction, Coulomb friction and Viscous damping affecting the damping mechanisms in a structure. Therefore it is difficult, and in some cases impossible, to describe the details of damping mechanisms by using mathematical tools. In order to overcome the difficulties arising when using different damping models, the equivalent viscous damping is used. The coefficient of equivalent viscous damping can be identified experimentally by measuring the structural response. During the past decades, many methods have been proposed for damping parameter identification employing time domain data (e.g. logarithmic decrement method) or frequency domain data (e.g. using frequency response functions). There are also time-frequency methods such as wavelet transform. This paper deals with identification of modal damping coefficients and natural frequencies of a structure using wavelet transform. The results obtained by using wavelet transform has a good agreement with those resulted from model updating in lower modes.

The resolution of seismic data decreases due to tuning effect, attenuation, and absorption and has always been one of the challenges for the interpreters. We can perform resolution enhancement in many ways, and in this regard, spiking deconvolution is the most critical approach. The ideal method to increase the resolution is to magnify or retrieve weak high frequency signals to provide a broad frequency spectrum, and therefore, substantially compressed signals. In this paper, a Hilbert-wavelet derived method has been investigated for this purpose. First, the input data using the wavelet transform (WT) are decomposed and enhanced by the Hilbert transform (HT) in the wavelet domain. The inverse WT yields compressed data based on current frequencies without any estimate and approximations. The Lack of significant distortions of recovered frequencies makes this process more distinctive than introduced methods. In this study, complex wavelet transform (CWT) and undecimated discrete wavelet transform (UDWT) are used. In fact, UDWT provides increased resolution, but on the opposite side, CWT presents impressive results. It deliveres fewer artifacts because of its time-frequency representations due to its unique combination of shift-invariant. This approach has been proposed and implemented to shot gathers after preliminary processing.

Analysis and interpretation of medical images are of clinical importance for medical diagnosis and treatment while they also have technical implications for computer vision and pattern recognition. In this context, one of the most fundamental issues is the detection of object boundaries, which is often useful for further processes such as organ/tissue recognition, image registration, motion analysis, measurement of anatomical and physiological parameters, etc. Although one of the best methods of edge detection is based on wavelet transform, the standard wavelet transform has its own shortcomings such as lack of shift invariant and lack of directional selectivity in sub-bands in multidimensional applications. The discrete complex wavelet transform, which is based on complex mother wavelet, not only overcomes these shortcomings but has acceptable redundancy and complexity as well. It is especially useful for multidimensional situations and for high accuracy applications such as medical image processing. In this paper, the shortcomings of ordinary wavelet transform are initially investigated and comparisons are made between the standard wavelet and the complex wavelet. Then, the discrete complex wavelet domain is applied for image enhancement and edge detection of noisy images. The simulation results show that our method exhibits a better performance, especially in noisy cases, as compared with the standard wavelet and spatial methods.

Spatial aliasing is unavoidable in some seismic data acquisitions and has serious adverse impacts on the performance of multichannel data processing and interpretation. So far, many methods have been used for removing spatial aliasing, such as interpolation methods of seismic traces by various transforms like Radon transform. In this paper, a new method for complete removal of spatial aliasing using stationary wavelet transform is presented. Our method has several advantages over previous ones such as, fixing the number of traces, avoiding transfiguration of wavelet shape that constructs the traces, minimizing the phase shift in seismic events, and reducing the run time.