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This paper addresses the features of Hough Transform (HT) butterflies suitable for image-based segment detection and measurement. The full segment parameters such as the position, slope, width, length, continuity, and uniformity are related to the features of the HT butterflies. Mathematical analysis and experimental data are presented in order to demonstrate and build the relationship between the measurements of segments and the features of HT butterflies. An effective method is subsequently proposed to employ these relationships in order to discover the parameters of segments. Power line inspection is considered as an application of the proposed method. The application demonstrates that the proposed method is effective for power line inspection, especially for corner detection when they cross poles.

MANETs are exposed to numerous security threats due to their characteristic features, which include absence of centralised control unit, open communication media, infrastructure-less and dynamic topology. One of commonest attack is known as black hole attack, which mostly targets the MANETs reactive routing protocols, such as AODV and DSR. Simulation scenarios of AODV and DSR based MANET were conducted using Network Simulator 2 (NS-2) and NS-3, while introducing the black hole attack in each of the scenarios, to analyse the protocols' performances. The different scenarios are generated by changing the mobility (locations) of the nodes. The performance metrics that are used to do the analysis are throughput, end-to-end delay and packet delivery ratio. The simulation results showed that the performance of both AODV and DSR degrades in the presence of black hole attack. Throughput and packet delivery ratio decrease when the network is attacked by black hole, because the malicious node absorbs or discards some of the packets. End-to-end delay is also reduced in the presence of a black hole attack because a malicious node pretends to have a valid route to a destination without checking the routing table, and therefore shortens the route discovery process. The results also showed that throughput decreases slightly when mobility of the nodes is increased in the network. The increase in the speed of the nodes decreases both packet delivery ratio and end-to-end delay. The closer the black hole node was to the source node requesting the transmission, the worse the impact. A focused analysis on AODV indicates that, even with the introduction of relatively few black hole nodes to the network, there still exist a potential to bring significant disruptions to communication.

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One of the biggest problems of ODL teaching/learning is that lecturers cannot get the feedback from students in time and modify the teaching materials and styles according to the interaction of students. The burgeoning Brain Computer Interface (BCI) created the possibility of assessing the activities of working memory which is closely related to the knowledge accepting (learning, understanding) efficiency. This research aims to build a real-time teaching and learning efficiency assessing system based on the technique of electroencephalograph (EEG, a kind of non-invasive BCI). The activities of working memory is detected by the system when students learning, based on which both sides of lecturers and students, can modify teaching/learning materials and styles. So a relative higher efficiency of knowledge delivery will be created.

Wireless sensor networks (WSNs) are one of the most essential technologies in the 21st century due to their increase in various application areas and can be deployed in areas where cable and power supply are difficult to use. However, sensor nodes that form these networks are energyconstrained because they are powered by non-rechargeable small batteries. Thus, it is imperative to design a routing protocol that is energy efficient and reliable to extend network lifetime utilization. In this article, we propose an improved ant colony optimization algorithm: a technique for extending wireless sensor networks lifetime utilization called AMACO. We present a new clustering method to avoid the overhead that is usually involved during the election of cluster heads in the previous approaches and energy holes within the network. Moreover, fog computing is integrated into the scheme due to its ability to optimize the limited power source of WSNs and to scale up to the requirements of the Internet of Things applications. All the data packets received by the fog nodes are transmitted to the cloud for further analysis and storage. An improved ant colony optimization (ACO) algorithm is used to construct optimal paths between the cluster heads and fog nodes for a reliable end-to-end data packets delivery. The simulation results show that the network lifetime in AMACO increased by 22.0%, 30.7%, and 32.0% in comparison with EBAR, IACO-MS, and RRDLA before the first node dies (FND) respectively. It increased by 15.2%, 18.4%, and 33.5% in comparison with EBAR, IACO-MS, and RRDLA before half nodes die (HND) respectively. Finally, it increased by 28.2%, 24.9%, and 58.9% in comparison with EBAR, IACO-MS, and RRDLA before the last node dies (LND) respectively.

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