Iris research is focused on developing techniques for identifying and locating relevant biometric features, accurate segmentation and efficient computation while lending themselves to compression methods. Most iris segmentation methods are based on complex modelling of traits and characteristics which, in turn, reduce the effectiveness of the system being used as a real time system. This paper introduces a novel parameterized technique for iris segmentation. The method is based on a number of steps starting from converting grayscale eye image to a bit plane representation, selection of the most significant bit planes followed by a parameterization of the iris location resulting in an accurate segmentation of the iris from the origin

The influence of 5-10 kHz audio frequency on the power dissipation in ac discharge of argon gas was studied experimentally, at pressures 50-80 mTorr and electrodes separation 10 cm (pd range 0.5-0.8 Torr.
cm). The measurements have shown that the discharge behavior in the ac circuit is equivalent to a series RC circuit. It is observed that the variation curve of discharge power P with the frequency f is approximately has a Gaussian shape. It is also observed that the curve of Pm- pd is the inverse of Paschen curve, where Pm is the maximum power in the frequency range. The time of breakdown is estimated from the curve of P- f.

Objectives: To assess the changes in blood vessel stiffness and digital pulse wave amplitude because of flowmediateddilatation, and to explore how these two variables change when endothelial dysfunction isexperimentally induced.Method: The experimental study was conducted at the departments of physiology at the College of Medicine,Mustansiriyah University, and the College of Medicine, Al-Iraqia University, Baghdad, Iraq, from October 14, 2021, toMay 31, 2022, and comprised healthy young males who were subjected to the flow-mediated dilatation techniqueon the left brachial artery. Pulse transit time and the amplitude of the digital pulse wave were measured duringreactive hyperaemia for 2.5 minutes from the left middle finger using a

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesi

An accurate assessment of the pipes’ conditions is required for effective management of the trunk sewers. In this paper the semi-Markov model was developed and tested using the sewer dataset from the Zublin trunk sewer in Baghdad, Iraq, in order to evaluate the future performance of the sewer. For the development of this model the cumulative waiting time distribution of sewers was used in each condition that was derived directly from the sewer condition class and age data. Results showed that the semi-Markov model was inconsistent with the data by adopting ( 2 test) and also, showed that the error in prediction is due to lack of data on the sewer waiting times at each condition state which can be solved by using successive conditi

Until recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15