In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

This study employs wavelet transforms to address the issue of boundary effects. Additionally, it utilizes probit transform techniques, which are based on probit functions, to estimate the copula density function. This estimation is dependent on the empirical distribution function of the variables. The density is estimated within a transformed domain. Recent research indicates that the early implementations of this strategy may have been more efficient. Nevertheless, in this work, we implemented two novel methodologies utilizing probit transform and wavelet transform. We then proceeded to evaluate and contrast these methodologies using three specific criteria: root mean square error (RMSE), Akaike information criterion (AIC), and log

Abstract

 The objective of image fusion is to merge multiple sources of images together in such a way that the final representation contains higher amount of useful information than any input one.. In this paper, a weighted average fusion method is proposed. It depends on using weights that are extracted from source images using counterlet transform. The extraction method is done by making the approximated transformed coefficients equal to zero, then taking the inverse counterlet transform to get the details of the images to be fused. The performance of the proposed algorithm has been verified on several grey scale and color test  images, and compared with some present methods.

conventional FCM algorithm does not fully utilize the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The spatial function is the summation of the membership functions in the neighborhood of each pixel under consideration. The advantages of the method are that it is less
sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. This technique is a powerful method for noisy image segmentation. 

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

The increased size of grayscale images or upscale plays a central role in various fields such as medicine, satellite imagery, and photography. This paper presents a technique for improving upscaling gray images using a new mixing wavelet generation by tensor product. The proposed technique employs a multi-resolution analysis provided by a new mixing wavelet transform algorithm to decompose the input image into different frequency components. After processing, the low-resolution input image is effectively transformed into a higher-resolution representation by adding a zeroes matrix. Discrete wavelets transform (Daubechies wavelet Haar) as a 2D matrix is used but is mixed using tensor product with another wavelet matrix’s size. MATLAB R2021

ABSTRACT Background: The Iraqi hospital witnessed numerous violence incidents against medical staff working in emergency department and range from verbal to physical violence. High frequency of these attacks urged the Iraqi doctors for migration. Aim of study: To identify the prevalence of workplace violence against medical staff and to and study the risk factors related to work place violence. Materials and methods: A descriptive cross sectional study carried out among a sample of 300 medical

In this work preparation of antireflection coating with single layer of MgO using pulsed laser deposition (PLD) method which deposit on glass substrate with different thicknesses (90 and 100) nm annealed at temperature 500 K was done.
The optical and structural properties (X-ray diffraction) have been determined. The optical reflectance was computed with the aid of MATLAB over the visible and near infrared region. Results shows that the best result obtained for optical performance of AR'Cs at 700 shots with thickness 90 nm nanostructure single layer AR'Cs and low reflection at wavelength 550 nm.

Doppler broadening of the 511 keV positron annihilation ??? ? was used to estimate the concentration of defects ?? different deformation levels of pure alnminum samples. These samples were compressed at room temperature to 15, 22, 28, 38,40, and 75 % thickness reduction. The two-state ^sitron-trapping model has been employed. 'I he s and w lineshape parameters were measured using high-resolution gamma spectrometer with high pure germanium detector of 2.1 keV resolution at 1.33 MeV of 60Co. The change of defects concentration (Co) with the deformation level (e) is found to obey an empirical formula of the form Cd - A £ B where A and ? are positive constants that depend mainly on the deformation procedure and the temperature at which the def