This paper proposed a new method for network self-fault management (NSFM) based on two technologies: intelligent agent to automate fault management tasks, and Windows Management Instrumentations (WMI) to identify the fault faster when resources are independent (different type of devices). The proposed network self-fault management reduced the load of network traffic by reducing the request and response between the server and client, which achieves less downtime for each node in state of fault occurring in the client. The performance of the proposed system is measured by three measures: efficiency, availability, and reliability. A high efficiency average is obtained depending on the faults occurred in the system which reaches to 92.19%, availability 92.375%, and reliability 100%. The proposed system managed five devices. The NSFM implemented using Java and C# languages.
In this paper, we investigate two stress-strength models (Bounded and Series) in systems reliability based on Generalized Inverse Rayleigh distribution. To obtain some estimates of shrinkage estimators, Bayesian methods under informative and non-informative assumptions are used. For comparison of the presented methods, Monte Carlo simulations based on the Mean squared Error criteria are applied.
A tunable band pass filter based on fiber Bragg grating sensor using an in-fiber Mach-Zender interferometer with dual micro-cavities is presented. The micro-cavity was formed by splicing together a conventional single-mode fiber and a solid core photonic crystal fiber (SCPCF) with simple arc discharge technique. Different parameters such as arc power, length of the SCPCF and the overlap gap between samples were considered to control the fabrication process. The ellipsoidal air-cavity between the two fibers forms Fabry-Perot cavity. The diffraction loss was very low due to short cavity length. Ellipsoidal shape micro-cavities were experimentally achieved parallel to the propagation axis having dimensions of (24.92 – 62.32) μm of width
... Show MoreThe ability of the human brain to communicate with its environment has become a reality through the use of a Brain-Computer Interface (BCI)-based mechanism. Electroencephalography (EEG) has gained popularity as a non-invasive way of brain connection. Traditionally, the devices were used in clinical settings to detect various brain diseases. However, as technology advances, companies such as Emotiv and NeuroSky are developing low-cost, easily portable EEG-based consumer-grade devices that can be used in various application domains such as gaming, education. This article discusses the parts in which the EEG has been applied and how it has proven beneficial for those with severe motor disorders, rehabilitation, and as a form of communi
... Show MoreUsing watermarking techniques and digital signatures can better solve the problems of digital images transmitted on the Internet like forgery, tampering, altering, etc. In this paper we proposed invisible fragile watermark and MD-5 based algorithm for digital image authenticating and tampers detecting in the Discrete Wavelet Transform DWT domain. The digital image is decomposed using 2-level DWT and the middle and high frequency sub-bands are used for watermark and digital signature embedding. The authentication data are embedded in number of the coefficients of these sub-bands according to the adaptive threshold based on the watermark length and the coefficients of each DWT level. These sub-bands are used because they a
... Show MoreThe drill bit is the most essential tool in drilling operation and optimum bit selection is one of the main challenges in planning and designing new wells. Conventional bit selections are mostly based on the historical performance of similar bits from offset wells. In addition, it is done by different techniques based on offset well logs. However, these methods are time consuming and they are not dependent on actual drilling parameters. The main objective of this study is to optimize bit selection in order to achieve maximum rate of penetration (ROP). In this work, a model that predicts the ROP was developed using artificial neural networks (ANNs) based on 19 input parameters. For the
Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software
... Show MoreRealizing robust interconnectivity in a rapidly changing network topology is a challenging issue. This problem is escalating with the existence of constrained devices in a vehicular environment. Several standards have been developed to support reliable communication between vehicular nodes as the IEEE 1609 WAVE stack. Mitigating the impact of security/mobility protocols on limited capability nodes is a crucial aspect. This paper examines the burden of maintaining authenticity service that associated with each handover process in a vehicular network. Accordingly, a network virtualization-based infrastructure is proposed which tackles the overhead of IEEE 1906 WAVE standard on constrained devices existed in vehicular network. The virtualized
... Show MoreSpecialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give
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