Engineering and Technology Journal

The purpose of the study is to inhibit the corrosion rate using the (LSP) laser shock processing technology with an ND-YAG laser utilizing the Q-switched technique with a wavelength of 1046 nm for stainless steel 304 alloy. Laser shock processing (LSP) is a novel surface treatment approach for strengthening metal materials that use a high peak power, a brief pulse, and cold hardening. The laser parameters’ effect includes laser pulse energy and pulse repetition rate on the surface properties. The qualities of sample surfaces were investigated, including surface roughness and micro-hardness. The X-ray fluorescence technique was used to analyze the chemical composition of this alloy. The corrosion rate was measured using the polarization method. In particular, pitting corrosion was the option we picked. According to the findings, laser shock processing appears to considerably boost the micro-hardness of the LSP-treated sample. The corrosion causes a decrease in the rate. The result revealed that the corrosion current density was decreased, and the corrosion potential was shifted when the laser pulse energy and the pulse repetition rate were increased. When the laser energy was 920 mj and the pulse repetition rate was 6 Hz, we discovered the lowest rate of corrosion. Digital images are usually achieved by changing continuous signals to digital format. Many factors can affect the quality of an image, such as knowledge requirements, causing noise, low contrast, and badly-defined boundaries, among others. Specifically, we compared the results with those of the image processing.

In this paper, crystalline optical fibers were used as a sensor for sensing the zinc ion concentration using the image processing technique. The image of the laser spot transmitted through the optical fiber crystal sensor for each concentration of zinc ion solution. The sensor was made by welding a piece of LMA-10 crystal optical fiber from both ends of a single-mode optical fiber to obtain an SM-PCF-SM type sensor. And it is possible to distinguish between one concentration and another by studying the change of the images obtained as a result of changing the concentration of the zinc ion. The sensitivity of the manufactured sensor was about 73.47%.

Moving objects detection, type recognition, and traffic analysis in video-based surveillance systems is an active area of research which has many applications in road traffic monitoring. This paper is on using classical approaches of image processing to develop an efficient algorithm for computer vision based on traffic surveillance system that can detect and classify moving vehicles, besides serving some other traffic analysis issues like finding vehicles speed and heading, tracking specified vehicles, and finding traffic load. The algorithm is designed to be flexible for modification to fulfill the changes in design objectives, having limited computation time, giving good accuracy, and serves inexpensive implementation. A 92% of success is achieved for the considered test, with the missed cases being abnormal that are not defined to the algorithm. The computation time, with a platform (hardware and software) dependent, the algorithm took to produce results was parts of milliseconds. A CNN based deep learning classifier was built and evaluated to judge the feasibility of involving a modern approach in the design for the targeted aims in this work. The modern NN based deep learning approach is very powerful and represents the choice for many very sophisticated applications, but when the purpose is restricted to limited requirements, as it is believed the case is here, the reason will be to use the classical image processing procedures. In making choice, it is important to consider, among many things, accuracy, computation time, and simplicity of design, development, and implementation.

Robot Vision is one of the most important applications in Image processing. Visual interaction with the environment is a much better way for the robot to gather information and react more intelligently to the variations of the parameters in that environment. A common example of an application that depends on robot vision is that of Pick-And-Place objects by a robotic arm. This work presents a method for identifying an object in a scene and determines its orientation. The method presented enables the robot to choose the best-suited pair of points on the object at which the two-finger gripper can successfully pick the object. The scene is taken by a camera attached to the arm’s end effector which gives 2D images for analysis. The edge detection operation was used to extract a 2D edge image for all the objects in the scene to reduce the time needed for processing. The methods proposed showed accurate object identification which enabled the robotic to successfully identify and pick an object of interest in the scene

Wavelet analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast. Here,  experimentally demonstrate the abilities of the discrete wavelet transform with Daubechies basis functions for improving the quality of noisy images. In this research, two methods have been compared to modify the coefficients using a soft and hard threshold to improve the visual fineness of noisy images depending on the Root-Mean-Square error (RMS). The low RMS value and better noise reduction are found in the soft threshold methods based on Daubechies wavelet (db8) for the first image RMS=0.101 and the second example RMS=0.109.

Wavelet-analysis has become a powerful tool for denoising images. It represents a new way to achieve better noise reduction and increased contrast. Here, experimentally demonstrate abilities of discrete wavelet transform with Daubechies basis functions for improving the quality of noisy images.in this research two methods has been compaired for modify the coefficients using soft and hard threshold to improv the visual fineness of noisy image depend on Root-Mean-Square error (RMS). The low RMS value and better noise reduction find in soft threshold method which is based on Daubechies wavelet (db8) for first example image RMS=0.101 and second example RMS=0.109

This study was conducted for the purpose of monitoring changes in the marshes of southern Iraq, depend on image processing techniques for Landsat images for the period from 1990 to 2015.Landsat satellite images such as TM, ETM+, and LDCM for years 1990,2000, and 2015 in addition to set of maps were used, and then all these data were analysed and extracted the information from it by using ERDAS EMAGINE 2014 program also to extract the final maps layout the ARC GIS 10.2 program was used .Two important indices were extracted from satellite images, Transformed Normalized Difference Vegetation Index (TNDVI) and Normalized Difference Water Index (NDWI) for extract natural vegetation and water in study area. Supervised classification has been used to product three land cover maps for study area. After conducting all necessary analyses, the final results showed that the deterioration has happened largely in the waters of marshes and natural vegetation area in the period from 1990 to 2000 and then this deterioration was beginning decrease gradually and marshes began to recover from 2000 to 2015, there is increase in the surface area of waters of the marshes and natural vegetation in year 2015 than in year 2000, but this increase does not reach to the area of water and natural vegetation in year 1990.

The noise is any undesired signal that contaminates an image. This paper proposes an algorithm for color image noise detection of several types of noise, namely; Gaussian, Salt and Pepper and Speckle.
This algorithm uses a method of generating a square matrix from original image, called a Recursive Matrix (RM)
This RM was used successfully in detecting the noisy or noisy-free image. The first step is to analyze the three bands monochrome image (color image) to Red, Green and Blue images, then deal with each image as a grey-scale image which is represented as 2-Dimenssion matrix. The second Step is to construct the RM to each monochrome image, then to calculate the standard deviation (std.) for each RM to distinguish between noisy and pure image by using objective testdepending on Std. threshold. In the third step, the subjective test is used to the same image by plotting the image with its RM in 3-Dimensions, for both pure and noisy images. The proposed algorithm gives a perfect detection of noise in 50 color images as a case study used in this algorithm.

This research proposed vision measurement system which consists of a camera carried on hand of a robot, which captured 2D image to the object from two sides with a constant distance of the objects. To achieve this work several experimental steps are needed: First step includes calibrating the camera by using a standard block to find the best distance between the camera and the object.The best result of a distance is (410)mm. The second step consists of using MATLAB 7.12.0 (R2011a) program to achieve image processing to get some digital information (number of pixels in each row and column), using the proposed line by line scanning algorithm, to extract 2D object dimensions. The resulted dimensions are found closer to real object dimensions that are measured using a digital vernier and 3d digital probe. Last step includes 2D image manipulating using the proposed algorithms to reconstruct the 3D objects depending on the resulted information (number of pixels).

The concepts of image processing, computer vision and computer graphics have very important using in different laterals science. This research proposed vision measurement system which consists of a camera carried on hand of a robot, which captured 2D image to the object from two sides with a constant distance of the objects. To achieve this work several experimental steps are needed: First step including calibrating the camera by using a standard block to find the best distance between the camera and the object, the best result of a distance is (410)mm. The second step consist of using MATLAB 7.12.0 (R2011a) program to achieve image processing to get some digital information (number of pixels in each row and column), by scanning the image line by line, to extract 2D object dimensions. The resulted dimensions are found closer to real object dimensions that are measured using a digital vernier and 3d digital probe. Last step includes 2D image manipulating to reconstruct the 3D objects depending on the resulted information (number of pixels).

Neural network technology plays an important role in the development of new
medical diagnostic assistance or what is known as “computer aided” that based on
image recognition.Thispaper study the method used integration of back propagation
neural network and Particle Swarm Optimizing (PSO) in parts of recognition the XRay
of lungs for two disease cases (cancer and TB) along with the normal case. The
experiments show that the improvement of algorithms for recognition side has
achieved a good result reached to 88.398% for input image size 1024 pixel and 500
population size. The efficiency and recognition testes for training method was
performed and reported in this paper

To extract features from digital images, it is useful to be able to find simple
shapes (straight lines, circles, ellipses, etc.).
In order to achieve this goal, one can be able to detect a group of pixels that are on
a straight line or a smooth curve. That is what a Hough transform is supposed to
do.
Since the Hough transform is a special case of Radon transform then line detection
process is accomplished using Radon transform.
In this paper, software for line detection using Radon transform has been designed
and implemented. Then the implemented software is tested under many conditions
and circumstances, the results are discussed, and many points are concluded from
the results.