Abstract:

The real-time service such as voice over internet protocol (VoIP) is considered the best type of conversational voice due to ability and the possibility of making a VoIP calls at any time and from anywhere with the lowest cost. In this paper, we explore the performance of UMTS and WiMAX networks for VoIP service. This work focuses on VoIP quality when sent under various network loads such as HTTP, Email, FTB and Video Conferencing. In UMTS network, the VoIP was congested with HTTP traffic, while in WiMAX network the VoIP was congested with many types of traffic such as HTTP, Email, FTP and Video Conferencing. These traffic flow effects on voice traffic are analyzed and evaluated in terms of end-to-end delay, jitter and packet loss with service quality (QoS). All simulation scenarios are performed with OPNET modeller 14.5.

Medical images modalities are extensively adapted and used for disease diagnosis. These imaging modalities include computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and X-ray… etc. The problem associated with medical images is the storage space and bandwidth required for archiving and transmission of this kind of images. Thus image compression is a key factor to reduce the bit rate for transmission or storage while maintaining an acceptable reproduction quality In this paper, an efficient ROI compression approach using hybrid lossy lossless compression technique will be presented. This method is based on applying discreet wavelet transform (DWT) as a lossy method and grayscale matrix (GSM) coding algorithm as lossless method. The results show that the performance of the proposed method is much better than the other existing techniques especially with smaller regions of interest.

Information security management is a necessity for all institutions and enterprises that regard company information as valuable assets. Developing, auditing and managing information security depends upon professional expertise in order to achieve the desired information security governance. This research seeks the key skills required for the position of information security management as well as the methods to develop these skills through professional training programs. The study adopts the Delphi method which requires building a list of items through a literature survey and involves experts with certain expertise to modify the list until a consensus on less than 20% of the items is reached. Through completing three rounds of the Delphi technique - data collection, relevance voting and ranking - sixteen skills are shortlisted as the key skills. In the final list, the majority belong to core information security skills, and the top two skills belong to project/process management skills and risk management skills, indicating the importance of these skills for the information security manager role. In addition, a series of related professional training programs and certifications are surveyed, the outcome of which highlights a number of most comprehensive and appropriate programs to develop these determined skills.

Dust storms are one of the natural hazards whose incidence has increased in the recent years over Sahara desert, Australia and northern China. Thus, it is important to know the causation, movement and radiation effects of dust storms. Satellite remote sensing is the most common method for monitoring Dust Storms but its use over sandy ground is still limited as they have similar characteristics. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data. However, there have been fewer studies for detecting dust storms at night. The key elements of this study are to use a back-propagation artificial neural network with Brightness Temperature of band 31 and four Brightness Temperature Differences calculated using data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop a method for detecting dust storms during both day and night. Results have shown that the method can detect dust storms at both day and night and also over different land surfaces. 

Dust storms are one of the natural phenomena, which have increased in frequency in recent years in North Africa, Australia and northern China. Satellite remote sensing is the common method for monitoring dust storms but its use for identifying dust storms over sandy ground is still limited as the two share similar characteristics. In this study, an artificial neural network (ANN) is used to detect dust storm using 46 sets of data acquired between 2001 and 2010 over North Africa by the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites. The ANN uses image data generated from Brightness Temperature Difference (BTD) between bands 23 and 31 and BTD between bands 31 and 32 with three bands 1, 3, and 4, to classify individual pixels on the basis of their multiple-band values. In comparison with the manually detection of dust storms, the ANN approach gave …

Dust storms are one of the natural hazards, which have increased in frequency in the recent years over Sahara desert, Australia, the Arabian Desert, Turkmenistan and northern China, which have worsened during the last decade. Dust storms increase air pollution, impact on urban areas and farms as well as affecting ground and air traffic. They cause damage to human health, reduce the temperature, cause damage to communication facilities, reduce visibility which delays both road and air traffic and impact on both urban and rural areas. Thus, it is important to know the causation, movement and radiation effects of dust storms. The monitoring and forecasting of dust storms is increasing in order to help governments reduce the negative impact of these storms. Satellite remote sensing is the most common method but its use over sandy ground is still limited as the two share similar characteristics. However, satellite remote sensing using true-colour images or estimates of aerosol optical thickness (AOT) and algorithms such as the deep blue algorithm have limitations for identifying dust storms. Many researchers have studied the detection of dust storms during daytime in a number of different regions of the world including China, Australia, America, and North Africa using a variety of satellite data but fewer studies have focused on detecting dust storms at night. The key elements of this present study are to use data from the Moderate Resolution Imaging Spectroradiometers on the Terra and Aqua satellites to develop more effective automated method for detecting dust storms during both day and night and generate a MODIS dust storm database.

One of the problems worsened by climate change is the occurrence of Sand Dust Storms (SDS), which are dry winds carrying sand. In recent years the numbers of SDSs have increased in North Africa, Australia and northern China. Satellite remote sensing (SR) is the main method for monitoring SDS as they happen. However its use for identifying sand dust storms over sandy ground such as Saharan desert is still limited as both materials share similar characteristics. In this paper, we propose a new approach to distinguish dust storms cloud from sandy ground and water cloud using Moderate Resolution Imaging Spectroradiometer (MODIS) bands.