Ayman Altameem

Medical Image Segmentation Methods, Algorithms, and Applications

ABSTRACT Medical images have made a great impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. Many image segmentation methods for medical image analysis have been presented in this paper. In this paper, we have described the latest segmentation methods applied in medical image analysis. The advantages and disadvantages of each method are described besides examination of each algorithm with its application in Magnetic Resonance Imaging and Computed Tomography image analysis. Each algorithm is explained separately with its ability and features for the analysis of grey-level images. In order to evaluate the segmentation results, some popular benchmark measurements are presented in the final section.

Internet of Vehicles: Motivation, Layered Architecture, Network Model, Challenges, and Future Aspects

Internet of Things is smartly changing various existing research areas into new themes, including smart health, smart home, smart industry, and smart transport. Relying on the basis of “smart transport,” Internet of Vehicles (IoV) is evolving as a new theme of research and development from vehicular ad hoc networks (VANETs). This paper presents a comprehensive framework of IoV with emphasis on layered architecture, protocol stack, network model, challenges, and future aspects. Specifically, following the background on the evolution of VANETs and motivation on IoV an overview of IoV is presented as the heterogeneous vehicular networks. The IoV includes five types of vehicular communications, namely, vehicle-to-vehicle, vehicle-to-roadside, vehicle-to-infrastructure of cellular networks, vehicle-to-personal devices, and vehicle-to-sensors. A five layered architecture of IoV is proposed considering functionalities and representations of each layer. A protocol stack for the layered architecture is structured considering management, operational, and security planes. A network model of IoV is proposed based on the three network elements, including cloud, connection, and client. The benefits of the design and development of IoV are highlighted by performing a qualitative comparison between IoV and VANETs. Finally, the challenges ahead for realizing IoV are discussed and future aspects of IoV are envisioned.

ANALYSIS OF VISION BASED SYSTEMS TO DETECT REAL TIME GOAL EVENTS IN SOCCER VIDEOS

The purpose of this article is to analyze current vision-based systems from a soccer video semantic point of view such as video summarization, features analysis, and provision of augmented information. Currently, computer vision techniques are applicable in a challenging soccer context. Scene interpretation is performed based on the complexity of the semantic. For each area of vision-based systems, computer vision methodologies are analyzed along with their strengths and weaknesses. We have also investigated whether the existing approaches are equally applicable for real-time soccer video semantic analysis.

Evaluation of Current Dental Radiographs Segmentation Approaches in Computer-aided Applications

Abstract With a wide variety researches on Image segmentation techniques in biomedical and bioinformatics area, it is important to analyze the performance of these approaches in specific problems. Image segmentation is one of the most significant processes of dental X-ray image analysis. Therefore, to obtain the proper result, it is required to perform the accurate and efficient segmentation approach which proved itself in the aspect of X-ray image segmentation. The aim of this review paper is to understand the different image segmentation approaches which have been used for dental X-ray image analysis over the past studies. In this paper, different available approaches of dental X-ray image segmentation, reviewed and their advantages, disadvantages, and limitations are discussed.

Annotated comparisons of proposed preprocessing techniques for script recognition

Offline cursive script recognition and their associated issues are still fresh despite of last few decades’ research. This paper presents an annotated comparison of proposed and recently published preprocessing techniques with reported work in the offline cursive script recognition. Normally, in the offline script analysis, the input is a paper image or a word or a digit and the desired output is ASCII text. This task involves several preprocessing steps, and some of them are quite hard such as line removal from text, skew removal, reference line detection (lower/upper baselines), slant removal, scaling, noise elimination, contour smoothing and skeleton. Moreover, subsequent stage of segmentation (if any) and recognition is also highly dependent on these preprocessing techniques. This paper presents an analysis and annotated comparison of latest preprocessing techniques proposed by authors with those reported in the literature on IAM/CEDAR benchmark databases. Finally, future work and persist problems are highlighted.

Virtual machine security challenges: case studies

Currently Virtual Machines (VMs) have many applications and their use is growing constantly as the hardware gets more powerful and usage more regulated allowing for scaling, monitoring, portability, security applications and many other uses. There are many types of virtualization techniques that can be employed on many levels from simple sandbox to full fledged streamlined managed access. While scaling, software lifecycles and diversity are just some of security challenges faced by VM developers the failure to properly implement those mechanisms may lead to VM escape, host access, denial of service and more. There are many exploits found in the last couple of years which were fixed on latest versions but some systems are still running them and vulnerable as presented, mostly to host based attacks and some have dramatic consequences.

An intelligent fused approach for face recognition

Abstract Face detection plays important roles in many applications such as human–computer interaction, security and surveillance, face recognition, etc. This article presents an intelligent enhanced fused approach for face recognition based on the Voronoi diagram (VD) and wavelet moment invariants. Discrete wavelet transform and moment invariants are used for feature extraction of the facial face. Finally, VD and the dual tessellation (Delaunay triangulation, DT) are used to locate and detect original face images. Face recognition results based on this new fusion are promising in the state of the art.

Road-Aware Routing Strategies for Vehicular Ad Hoc Networks

The tremendous advancement in new wireless technologies has led to a renewed interest in vehicular ad hoc networks. However, due to the high mobility and dynamic nature of vehicular networks, great challenges exist for data delivery. In order to design efficient and smart routing strategies for stable communication, several different types of routing protocols have been integrated. One efficient type is the road-aware routing protocol which is based on road statistics (e.g., traffic density, intersections, and road segments). The main objective of the survey conducted in this paper is to discuss the recent road-aware routing protocols and categorize them according to different aspects. In addition, this review investigates the protocols in terms of their forwarding processes, routing metrics, recovery mechanisms, and performance. Moreover, routing challenges and comparisons are discussed.

Coverage Enhancement Algorithms for Distributed Mobile Sensors Deployment in Wireless Sensor Networks

Sensor nodes in wireless sensor networks are deployed to observe the surroundings for some phenomenon of interest. The fundamental issue in observing such environments is the area coverage which reflects how well the region is monitored. The nonuniform sensor nodes distribution in a certain region caused by random deployment might lead to coverage holes/gaps in the network. One of the solutions to improve area coverage after initial deployment is by sensor nodes mobility. However, the main challenge in this approach is how to increase area coverage with the least energy consumption. This research work aims to improve area coverage with minimal energy consumption and faster convergence rate. The Edge Based Centroid (EBC) algorithm is presented to improve the area coverage with faster convergence rate in a distributed network. The simulation based performance evaluations of the proposed algorithms are carried out in terms of area coverage, convergence rate, and energy efficiency. Compared to the existing works, EBC improved area coverage with faster convergence. It is concluded that the proposed algorithm has improved area coverage with faster convergence and minimal energy consumption.

VGDD: A virtual grid based data dissemination scheme for wireless sensor networks with mobile sink

Wireless sensor network (WSN) is considered as the enabling technology to bridge the gap between the physical and digital world. Some of the applications environments of WSN require a mobile sink to operate in the sensor field where delayed and/or partial data delivery might lead to inappropriate conclusions and thus require high quality of service in terms of latency and packets delivery ratio. Majority of existing mobile sink based data dissemination schemes aim to prolong network lifetime whereas few schemes improve the data delivery performance by employing multiple mobile sinks which add to the hardware and operating cost. In this paper we propose a virtual grid based data dissemination (VGDD) scheme that aims to optimize the tradeoff between network lifetime and data delivery performance while adhering to the low cost theme of WSN. Using the virtual structure, the proposed VGDD scheme follows a set of rules to disseminate sinks mobility updates in an energy efficient manner thereby maintaining nearly optimal routes. Furthermore, to cope with speeds variation of mobile sink, VGDD makes use of appropriate forwarder nodes for guaranteed data delivery. Simulation results reveal improved data delivery performance in terms of latency and data delivery ratio compared to existing work.