Abstract: (1) Background: A disabled student or employee in a certain university faces a large number of obstacles in achieving his/her ordinary duties. An interactive smart search and communication application can support the people at the university campus and Science Park in a number of ways. Primarily, it can strengthen their professional network and establish a responsive eco-system. Therefore, the objective of this research work is to design and implement a unified flexible and adaptable interface. This interface supports an intensive search and communication tool across the university. It would benefit everybody on campus, especially the People with Disabilities (PWDs). (2) Methods: In this project, three main contributions are presented: (A) Assistive Technology (AT) software design and implementation (based on user- and technology-centered design); (B) A wireless sensor network employed to track and determine user’s location; and (C) A novel event behavior algorithm and movement direction algorithm used to monitor and predict users’ behavior and intervene with them and their caregivers when required. (3) Results: This work has developed a comprehensive and universal application with a unified, flexible, and adaptable interface to support the different conditions of PWDs. It has employed an interactive smart based-location service for establishing a smart university Geographic Information System (GIS) solution. This GIS solution has been based on tracking location service, mobility, and wireless sensor network technologies. (4) Conclusion: The proposed system empowered inter-disciplinary interaction between management, staff, researchers, and students, including the PWDs. Identifying the needs of the PWDs has led to the determination of the relevant requirements for designing and implementing a unified flexible and adaptable interface suitable for PWDs on the university campus.
Abstract: In this paper, an authenticate live 3D point cloud video streaming system is presented, using a low cost 3D sensor camera, the Microsoft Kinect. The proposed system is implemented on a client-server network infrastructure. The live 3D video is captured from the Kinect RGB-D sensor, then a 3D point cloud is generated and processed. Filtering and compression are used to handle the spatial and temporal redundancies. A color histogram based conditional filter is designed to reduce the color information for each frame based on the mean and standard deviation. In addition to the designed filter, a statistical outlier removal filter is used. A certificate-based authentication is used where the client will verify the identity of the server during the handshake process. The processed 3D point cloud video is live streamed over a TCP/IP protocol to the client. The system is evaluated in terms of: compression ratio, total bytes per points, peak signal to noise ratio (PSNR), and Structural Similarity (SSIM) index. The experimental results demonstrate that the proposed video streaming system have a best case with SSIM 0.859, PSNR of 26.6 dB and with average compression ratio of 8.42 while the best average compression ratio case is about 15.43 with PSNR 18.5128 dB of and SSIM 0.7936.
Abstract: Vehicular ad hoc networks (VANETs) play a vital role in the success of self-driving and semi self-driving vehicles, where they improve safety and comfort. Such vehicles depend heavily on external communication with the surrounding environment via data control and Cooperative Awareness Messages (CAMs) exchanges. VANETs are potentially exposed to a number of attacks, such as grey hole, black hole, wormhole and rushing attacks. This work presents an intelligent Intrusion Detection System (IDS) that relies on anomaly detection to protect the external communication system from grey hole and rushing attacks. These attacks aim to disrupt the transmission between vehicles and roadside units. The IDS uses features obtained from a trace file generated in a network simulator and consists of a feed-forward neural network and a support vector machine. Additionally, the paper studies the use of a novel systematic response, employed to protect the vehicle when it encounters malicious behaviour. Our simulations of the proposed detection system show that the proposed schemes possess outstanding detection rates with a reduction in false alarms. This safe mode response system has been evaluated using four performance metrics, namely, received packets, packet delivery ratio, dropped packets and the average end to end delay, under both normal and abnormal conditions.
Abstract: The aim of this paper is to present a mobile agents model for distributed classification of Big Data. The great challenge is to optimize the communication costs between the processing elements (PEs) in the parallel and distributed computational models by the way to ensure the scalability and the efficiency of this method. Additionally, the proposed distributed method integrates a new communication mechanism to ensure HPC (High Performance Computing) of parallel programs as distributed one, by means of cooperative mobile agents team that uses its asynchronous communication ability to achieve that. This mobile agents team implements the distributed method of the Fuzzy C-Means Algorithm (DFCM) and performs the Big Data classification in the distributed system. The paper shows the proposed scheme and its assigned DFCM algorithm and presents some experimental results that illustrate the scalability and the efficiency of this distributed method.
Abstract: In 2009, Xu et al. presented a safe, dynamic, id-based on remote user authentication method that has several advantages such as freely chosen passwords and mutual authentication. In this paper, we review the Xu–Zhu–Feng scheme and indicate many shortcomings in their scheme. Impersonation attacks and insider attacks could be effective. To overcome these drawbacks, we propose a secure biometric-based remote authentication scheme using biometric characteristics of hand-geometry, which is aimed at withstanding well-known attacks and achieving good performance. Furthermore, our work contains many crucial merits such as mutual authentication, user anonymity, freely chosen passwords, secure password changes, session key agreements, revocation by using personal biometrics, and does not need extra device or software for hand geometry in the login phase. Additionally, our scheme is highly efficient and withstands existing known attacks like password guessing, server impersonation, insider attacks, denial of service (DOS) attacks, replay attacks, and parallel-session attacks. Compared with the other related schemes, our work is powerful both in communications and computation costs.
Abstract: A differentiation between all types of melanocytic and non-melanocytic skin lesions (MnM–SK) is a challenging task for both computer-aided diagnosis (CAD) and dermatologists due to the complex structure of patterns. The dermatologists are widely using pattern analysis as a first step with clinical attributes to recognize all categories of pigmented skin lesions (PSLs). To increase the diagnostic accuracy of CAD systems, a new pattern classification algorithm is proposed to predict skin lesions patterns by integrating the majority voting (MV–SVM) scheme with multi-class support vector machine (SVM). The optimal color and texture features are also extracted from each region-of-interest (ROI) dermoscopy image and then these normalized features are fed into an MV–SVM classifier to recognize seven classes. The overall system is evaluated using a dataset of 350 dermoscopy images (50 ROIs per class). On average, the sensitivity of 94%, specificity of 84%, 93% of accuracy and area under the receiver operating curve (AUC) of 0.94 are achieved by the proposed MnM–SK system compared to state-of-the-art methods. The obtained result indicates that the MnM–SK system is successful for obtaining the high level of diagnostic accuracy. Thus, it can be used as an alternative pattern classification system to differentiate among all types of pigmented skin lesions (PSLs).