Image pattern classification is considered a significant step for image and video processing. Although various image pattern algorithms have been proposed so far that achieved adequate classification, achieving higher accuracy while reducing the computation time remains challenging to date. A robust image pattern classification method is essential to obtain the desired accuracy. This method can be accurately classify image blocks into plain, edge, and texture (PET) using an efficient feature extraction mechanism. Moreover, to date, most of the existing studies are focused on evaluating their methods based on specific orthogonal moments, which limits the understanding of their potential application to various Discrete Orthogonal Moments (DOM

A Multiple System Biometric System Based on ECG Data

Gallium  selective  e lectrodes   based  on  12-crown-4  (12C4)  and benzo-15-crown-5   (B15C5)  and  plasticizers   dioctyl  phenyl phosphonate   (DOPP),   dibutyl  phosphate  (DBP),  dibutyl  phthalate (DBPH)  in  PVC  matrix  membranes  are  constructed.  Specific properties  of  the electrodes  are  studied  including  calibration  curve, slope  detection limit, concentration  

In recent years, the iris biometric occupies a wide interesting when talking about
biometric based systems, because it is one of the most accurate biometrics to prove
users identities, thus it is providing high security for concerned systems. This
research article is showing up an efficient method to detect the outer boundary of
the iris, using a new form of leading edge detection technique. This technique is
very useful to isolate two regions that have convergent intensity levels in gray scale
images, which represents the main issue of iris isolation, because it is difficult to
find the border that can separate between the lighter gray background (sclera) and
light gray foreground (iris texture). The proposed met

     NS-2 is a tool to simulate networks and events that occur per packet sequentially based on time and are widely used in the research field. NS-2 comes with NAM (Network Animator) that produces a visual representation it also supports several simulation protocols. The network can be tested end-to-end. This test includes data transmission, delay, jitter, packet-loss ratio and throughput. The Performance Analysis simulates a virtual network and tests for transport layer protocols at the same time with variable data and analyzes simulation results based on the network simulator NS-2.

      Graphene (Gr) decorated with silver nanoparticles (Ag NPs) were used to fabricate a wideband range photodetector. Silicon (Si) and porous silicon (PS) were used as a substrate to deposit Gr /Ag NPs by drop-casting technique. Silver nanoparticles (Ag NPs) were prepared using the chemical method. As well as the dispersion of silver NPs is achieved by a simple chemistry process on the surface of Gr.

    The optical, structure and electrical characteristics of AgNPs and Gr decorated with Ag NPs were characterized by ultraviolet-visible spectroscopy (UV-Vis), x-ray diffraction (XRD).  The X-ray diffraction (XRD) spectrum of Ag NPs exhibited 2θ values (38.1^o, 44.3 ^o, 64.5 ^o and 77.7

Optical fiber biomedical sensor based on surface plasmon resonance for measuring and sensing the concentration and the refractive index of sugar in blood serum is designed and implemented during this work. Performance properties such as signal to noise ratio (SNR), sensitivity, resolution and the figure of merit were evaluated for the fabricated sensor. It was found that the sensitivity of the optical fiber-based SPR sensor with 40 nm thick and 10 mm long Au metal film of the exposed sensing region is 7.5µm/RIU, SNR is 0.697, figure of merit is 87.2 and resolution is 0.00026. The sort of optical fiber utilized in this work is plastic optical fiber with a core diameter of 980 µm, a cladding of 20μm, and a numerical aperture of 0.

      Recognizing facial expressions and emotions is a basic skill that is learned at an early age and it is important for human social interaction. Facial expressions are one of the most powerful natural and immediate means that humans use to express their feelings and intentions. Therefore, automatic emotion recognition based on facial expressions become an interesting area in research, which had been introduced and applied in many areas such as security, safety health, and human machine interface (HMI). Facial expression recognition transition from controlled environmental conditions and their improvement and succession of recent deep learning approaches from different areas made facial expression representation mostly based on u