Reliable Mark-Embedded Algorithm for Verifying Archived/Encrypted Image Contents in Presence Different Attacks with FEC Utilizing Consideration

Wireless Personal Communications - Due to the widely spreading of fake news utilizing image manipulation and its bad effects, this paper investigates efficient image contents verification and...     

3-D boundary element method for cracks in a plate

New fundamental solutions which automatically satisfy boundary conditions at the interfaces of an elastic plate perfectly bonded to two elastic halfspaces are implemented in a 3-D boundary element method (BEM) for crack problems. The BEM features a new integration scheme for highly singular kernels. The capability is achieved through a part analytic and part numerical integration procedure, such that the analytic part of the integration is similar for all slip/opening variations, ‘Part-through’ elliptic cracks in an elastic plate with traction-free surfaces are analysed and the stress intensity factor (SIF) values along the crack front are found to compare favourably with widely accepted numerically obtained SIF results by Raju and Newman.¹     

Electronic data acquisition forms in construction management

Effective management of construction projects depends on good access to and control of data, especially data pertaining to cost and schedule control functions. However, the proper design and development of automated mechanisms have not had the facility to acquire and store quality data in a timely manner. The purpose of this paper, then, is to discuss a prototype, automated data acquisition system that was developed to support an integrated project management system. The data acquisition system is composed of an integrated set of electronic forms used in conjunction with a bar coding data-entry tool.     

Formation of C49-TiSi2 in flash memories: a nucleation controlled phenomenon?

The formation of Ti silicides has been examined in flash memories with 0.25 μm linewidth by X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. It has been observed that, after the first rapid thermal process and the selective metal etch, there is no silicide on the source and on a majority of drain contacts while C49-TiSi₂ is found on the gate. A pre-amorphisation implant increases drastically the formation of C49-TiSi₂ in the drain zone while modifications of annealing conditions have little impact. These results indicate that the formation of C49-TiSi₂ is most likely controlled by nucleation and that this nucleation is sensitive to both the width and the length of the reaction zone. The formation of a Ti rich silicide may play an important role in this nucleation by decreasing the driving force for the formation of C49-TiSi₂. Curiously enough, the formation of C49-TiSi₂ appears thus as a major concern for the salicide process in flash memories.     

Risk Assessment in Construction

This study is concerned with the assessment of risk for major construction activities. Risk has been defined as a measure of the probability, the severity, and the exposure of all hazards of an activity. A risk assessor model (RAM) was developed and computerized to determine the risk associated with a particular activity and the justification factor for a proposed remedy. Knowing the value of risk would help contractors identify the high risk of major construction activities and would enable them to allocate safety precautions in a more efficient manner.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

Electroplating of nickel-cadmium alloys from alkaline solutions

The electroplating of Ni-Cd alloy coatings was carried out from alkaline baths (pH 10). For comparison, electroplating of the parent metals, Ni and Cd, was performed individually under the same conditions. The cathodic current efficiency for codeposition was high and decreased with increasing current density. The codeposition process is an anomalous type of plating with Cd being the preferentially deposited metal. The Ni content in the deposits increased with increasing current density. This increase in Ni content improves the corrosion resistance and microhardness of the deposits. X-ray diffraction analysis showed that the deposits consist of a mixture of Ni, β, γ, γ¹ phases.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.