this work, a simple method was used to prepare the MnO2 nanoparticles. These nanoparticles then were characterized by several techniques, such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy (SEM) and atomic force microscope (AFM). The results showed that the diffraction peak of MnO2 nanoparticles was similar to that of standard data. The images of AFM and SEM indicated that the MnO2 nanorods were growing from the MnO2 nano spherical shape. PVA-pentaerythritol/MnO2 nanocomposite films were fabricated by evaporating casting method. The dielectric constant and loss tangent of P-Ery/MnO2 films were measured between 10 kHz and 1 MHz using LCR. As the content of MnO2 increased, the dielectric constant

The work in this paper focuses on the experimental confirming of the losses in photonic crystal fibers (PCF) on the transmission of Q-switched Nd:YAG laser. First HC-PCF was evacuated to 0.1 mbar then the microstructure fiber (PCF) was filled with He gas & gas. Second the input power and output power of Q-switched Nd:YAG laser was measured in hollow core photonic bandgap fiber (HCPCF). In this work loss was calculated in the hollow core photonic crystal fiber (HCPCF) filled with air then N2, and He gases respectively. It has bean observed that the minimum loss obtained in case of filling (HC-PCF) with He gas and its equal to 15.070 dB/km at operating wavelength (1040-1090) nm.

This research deals with the effects of welding variables using MIG/MAG spot by using Argon (Ar) gas and CO₂ to show their effect on the mechanical characteristics and microstructure of low alloy steel type DIN15Mo3 and determine the optimum condition for the process of welding ; current & time. The results show the possibility of using CO₂ and also Ar in low alloy steel welding with a little decrease in the shear force of not more than 13% for 4mm thickness and time 2sec. The shear force increased when using Ar instead of CO₂ to be , The shear force reach 36KN when using Ar at 2mm thickness  time of 8 sec and current of 220 Amp. , when used CO₂ instead of Ar d

With the continuous downscaling of semiconductor processes, the growing power density and thermal issues in multicore processors become more and more challenging, thus reliable dynamic thermal management (DTM) is required to prevent severe challenges in system performance. The accuracy of the thermal profile, delivered to the DTM manager, plays a critical role in the efficiency and reliability of DTM, different sources of noise and variations in deep submicron (DSM) technologies severely affecting the thermal data that can lead to significant degradation of DTM performance. In this article, we propose a novel fault-tolerance scheme exploiting approximate computing to mitigate the DSM effects on DTM efficiency. Approximate computing in hardw

We explore the transform coefficients of fractal and exploit new method to improve the compression capabilities of these schemes. In most of the standard encoder/ decoder systems the quantization/ de-quantization managed as a separate step, here we introduce new way (method) to work (managed) simultaneously. Additional compression is achieved by this method with high image quality as you will see later.

Building a system to identify individuals through their speech recording can find its application in diverse areas, such as telephone shopping, voice mail and security control. However, building such systems is a tricky task because of the vast range of differences in the human voice. Thus, selecting strong features becomes very crucial for the recognition system. Therefore, a speaker recognition system based on new spin-image descriptors (SISR) is proposed in this paper. In the proposed system, circular windows (spins) are extracted from the frequency domain of the spectrogram image of the sound, and then a run length matrix is built for each spin, to work as a base for feature extraction tasks. Five different descriptors are generated fro

Groupwise non-rigid image alignment is a difficult non-linear optimization problem involving many parameters and often large datasets. Previous methods have explored various metrics and optimization strategies. Good results have been previously achieved with simple metrics, requiring complex optimization, often with many unintuitive parameters that require careful tuning for each dataset. In this chapter, the problem is restructured to use a simpler, iterative optimization algorithm, with very few free parameters. The warps are refined using an iterative Levenberg-Marquardt minimization to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimization approach is eff

The computer vision branch of the artificial intelligence field is concerned with developing algorithms for analyzing video image content. Extracting edge information, which is the essential process in most pictorial pattern recognition problems. A new method of edge detection technique has been introduces in this research, for detecting boundaries.

           Selection of typical lossy techniques for encoding edge video images are also discussed in this research. The concentration is devoted to discuss the Block-Truncation coding technique and Discrete Cosine Transform (DCT) coding technique. In order to reduce the volume of pictorial data which one may need to store or transmit,

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura