Hesham Hashim Mohammed
@ntu.edu.iq
Computer Center
Northern Technical University
RESEARCH INTERESTS
Big Data
Scopus Publications
Scopus Publications
Hesham Hashim Mohammed, Shatha A. Baker, and Omar Ibrahim Alsaif
EJournal Publishing
Protecting maritime borders is crucial to ensuring overall border security. Law enforcement agencies make great use of analyzing images of underwater debris to gather intelligence and detect illicit materials. Underwater image improvement contributes to better data quality and analytical. Nevertheless, underwater image analysis poses greater challenges compared to analyzing images taken above the water, factors like refraction of light and darkness contribute to the degradation of underwater image quality. In this paper, a novel approach is proposed to enhance underwater images, the proposed approach involves splitting underwater colored image to its three basic components, Subsequently, a point spread function is created for each component to describes image blurring factor, The deblurring process is then applied by using wiener filter, the result sharped by sharping filter to clarify edges, contrast linear stretch is performed to improve contrast and visual details. and the resulting image is finally reassembled from the three basic components. The proposed method showed effective results in evaluating the main metrics and gave better results when compared to a number of different methods. These results prove the effectiveness of the proposed method and its ability to practical applications in improving image quality.
Dhuha Basheer Abdullah and Hesham Hashim Mohammed
IEEE
Clouds are the most powerful computation architecture; nevertheless, some applications are delay sensitive and need real time responses. Offloading tasks from user device to the cloud will take relatively long time and consumes network bandwidth. This motivates the appearance of fog computing. In fog, computing additional layer falls between user device layer and the cloud. Offloading tasks to fog layer will be faster and save network bandwidth. Fog computing has spread widely, but it is difficult to build and test such systems in real word. This led the developers to use fog simulation frameworks to simulate and test their own systems. In this paper, we adopt fog simulation formwork, which adds smart agent layer between user device and fog layer. The framework uses multilevel queue instead of single queue at the Ethernet layer, these queues are scheduled according to weighted round robin and tasks dispatched to theses queues according to the value of Type of Service (ToS) bits which falls at the second byte inside the IP header. The value of ToS bits given by the smart agent layer according to take constraints. Framework behavior compared with mFogSim framework and the results shows that the proposed framework has significantly decrease the delay on both brokers and fog nodes. furthermore, packet drop count and packet error rate are slightly improved
Dhuha Basheer Abdullah and Hesham Hashim Mohammed
IOP Publishing
Abstract Nowadays, there is a rapid development in vehicles world. Vehicles are equipped with smart systems as well as infotainment applications. But such systems consume vehicles’ computation or storage capacity. However, when the vehicle encounters a computation and/storage hungery applications or near real time applications that need high Quality of experience (QoE), it must offload it, either partially or entirely, to a more powerful and resourceful entity. At the beginnings this entity was a remote cloud. Although clouds are powerful in terms of computation and storage capacities, the process of task offloading to a remote cloud consumes the network bandwidth, which is not suitable to delay sensitive applications. As a solution, researchers propose to use cloudlets as third entity closer to the network edge. This will make the offloading much faster, but unfortunately due to the fact that cloudlets less computation and storage capacity than clouds, offloading will cause resource starvation. These factors motivate the appearance of Vehicular Cloud Computing (VCC). VCC proposes collecting the on-board units of multiple vehicles to form an on-ground cloud. This allows vehicles to offload their computational task to other vehicles in the vicinity. In this paper, we first provide a summery on concepts that are related to edge computing and task offloading process, and then we review a set of papers that use different approaches to execute computation offloading and scheduling.
Hesham Hashim Mohammed, Shatha A. Baker, and Ahmed S. Nori
IOP Publishing
Abstract In recent years hand geometric dependent biometric system has shown to be the quite acceptable biometric trait and suitable for security applications. It has been recognized as an effective means of authenticating identity in a variety of commercial applications as a result of better hardware and improved algorithms. This paper purpose a hand recognition system that extract 21 features for the right hand to identify and authorize persons. The system has two main parts, the first contain the data collection, explains the basic pre-processing required and how hand geometry characteristics like fingers length, width, coordinates of the base of the fingers, and palm width are extracted to derive the features used for discrimination, While the second part include the training and testing of three artificial neural networks to perform the recognition. After features extraction, the system uses three kinds of artificial neural networks in performing the recognition process, which are feed forward back propagation NN, Elman NN, and the cascade forward neural network NN. The proposed system shows that the Recognition Rate RR for the neural networks after testing were 95%, 92%, 88% respectively.
Shatha A. Baker, Hesham Hashim Mohammed, and Hanan A. Aldabagh
Universitas Ahmad Dahlan
The face-recognition system is among the most effective pattern recognition and image analysis techniques. This technique has met great attention from academic and industrial fields because of its extensive use in detecting the identity of individuals for monitoring systems, security and many other practical fields. In this paper, an effective method of face recognition was proposed. Ten person's faces images were selected from ORL dataset, for each person (42) image with total of (420) images as dataset. Features are extracted using principle component analysis PCA to reduce the dimensionality of the face images. Four models where created, the first one was trained using feed forward back propagation learning (FFBBL) with 40 features, the second was trained using 50 features with FFBBL, the third was trained using the same features but using Elman Neural Network. For each person (24) image used as training set for the neural networks, while the remaining images used as testing set. The results showed that the proposed method was effective and highly accurate. FFBBL give accuracy of (98.33,97.14) with (40, 50) features respectively, while Elman gives (98.33, 98.80) for with (40, 50) features respectively.