Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is concluded that, for the database images used in this work, the CBIR using hybrid technique is better for image retrieval because it has a higher match performance (100%) for each type of similarity measure so; it is the best one for image retrieval.
Porous Silicon (PSi) has been produced in this work by using Photochemical (PC) etching process by using a hydrofluoric acid (HF) solution. The irradiation has been achieved using quartz- tungsten halogen lamp. The influence of various irradiation times on the properties of PSi اmaterial such as layer thickness, etching rate and porosity was investigated in this work too.
The XRD has been studied to determine the crystal structure and the crystalline size of PSi material
In this work, MWCNT in the epoxy can be prepared at room temperature and thickness (1mm) at different concentration of CNTs powder. Optical properties of multi-walled carbon nanotubes (CNTs) reinforced epoxy have been measured in the range of (300-800)nm. The electronic transition in pure epoxy and CNT/epoxy indicated direct allowed transition. Also, it is found that the energy gap of epoxy is 4.1eV and this value decreased within range of (4.1-3.5)eV when the concentration of CNT powder increased from (0.001-0.1)% respectively.
The optical constants which include (the refractive index (n), the extinction coefficient (k), real (ε1) and imaginarily (ε2) part of dielectric constant calculated in the of (300-800)nm at different concent
Thin films of highly pure (99.999%) Tellurium was prepared by high vacuum technique (5*10-5torr), on glass substrates .Thin films have thickness 0.6m was evaporated by thermal evaporation technique. The film deposited was annealed for one hour in vacuum of (5*10-4torr) at 373 and 423 K. Structural and electrical properties of the films are studies. The x-ray diffraction of the film represents a poly-crystalline nature in room temperature and annealed film but all films having different grain sizes. The d.c. electrical properties have been studied at low and at relatively high temperatures and show that the conductivity decreases with increasing temperature at all range of temperature. Two types of conduction mechanisms were found to d
... Show MoreGround state energies and other properties of 2S shell for some atoms as Be(Z=4), B(Z=5), C(Z=6) and N(Z=7) were calculated by using Hartree-Fock wave function. We found the values of potential energies in hartree unit (3.8369, 6.78565, 10.18852 and 14.41089) respectively and the other proprieties like expectation values of the position < r1m > were in agreement with the published results. All the studied atomic properties were normalized.
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A study of the singlet and triplet states of two electron systems in the first excited state was performed using a simple quantum mechanical model, which assigns the 1s,and 2s orbital with two different variational parameters. Our results agree with a high level calculation used by Snow and Bills.
Abstract:Porous Silicon (PSi) has been produced in this work by using Photochemical (PC) etching process by using a hydrofluoric acid (HF) solution. The irradiation has been achieved using quartz- tungsten halogen lamp. The influence of various irradiation times on the properties of PSi اmaterial such as layer thickness, etching rate and porosity was investigated in this work too. The XRD has been studied to determine the crystal structure and the crystalline size of PSi material
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Ferrite with the general formula CuLayFe2-yO4 (where y=0.02, 0.04, 0.06, 0.08 and 0.1), were prepared by standard ceramic technique. The main cubic spinel structure phase for all samples was confirmed by x-ray diffraction patterns with the appearance of small amount of secondary phases. The lattice parameter results were 8.285-8.348 Å. X-ray density increased with La addition and showed values between 5.5826 – 5.7461gm/cm3. The Atomic Force Microscopy (AFM) showed that the average grain size was decreasing with the increase in La concentration. The Hall coefficient was found to be positive. It de |
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The main objective of present work is to describe the feasibility of friction stir welding (FSW) for
joining of low carbon steel with dimensions (3 mm X 80 mm X 150 mm). A matrix (3×3) of welding
parameters (welding speed and tool rotational speed) was used to see influence of each parameter on
properties of welded joint .Series of (FSW) experiments were conducted using CNC milling machine
utilizing the wide range of rotational speed and transverse speed of the machine. Effect of welding
parameters on mechanical properties of weld joints were investigated using different mechanical tests
including (tensile and microhardness tests ). Micro structural change during (FSW) process was
studied and different welding zones
The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h
... Show MoreCommunity detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective
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