Choosing a higher education course at university is not an easy task for students. A wide range of courses are offered by the individual universities whose delivery mode and entry requirements differ. A personalized recommendation system can be an effective way of suggesting the relevant courses to the prospective students. This paper introduces a novel approach that personalizes course recommendations that will match the individual needs of users. The proposed approach developed a framework of an ontologybased hybrid-filtering system called the ontology-based personalized course recommendation (OPCR). This approach aims to integrate the information from multiple sources based on the hierarchical ontology similarity with a view to enhancing the efficiency and the user satisfaction and to provide students with appropriate recommendations. The OPCR combines collaborative-based filtering with content-based filtering. It also considers familiar related concepts that are evident in the profiles of both the student and the course, determining the similarity between them. Furthermore, OPCR uses an ontology mapping technique, recommending jobs that will be available following the completion of each course. This method can enable students to gain a comprehensive knowledge of courses based on their relevance, using dynamic ontology mapping to link the course profiles and student profiles with job profiles. Results show that a filtering algorithm that uses hierarchically related concepts produces better outcomes compared to a filtering method that considers only keyword similarity. In addition, the quality of the recommendations is improved when the ontology similarity between the items' and the users' profiles were utilized. This approach, using a dynamic ontology mapping, is flexible and can be adapted to different domains. The proposed framework can be used to filter the items for both postgraduate courses and items from other domains.

[1] The paper describes an extensive wideband channel sounding measurement campaign to investigate signal propagation through vegetation. The measurements have been conducted at three frequencies (1.3, 2 and 11.6 GHz) at sites with different measurement geometries and tree species. The data have been used to evaluate current narrowband empirical vegetation attenuation models and study the prevailing propagation mechanisms. Evaluation of the modified exponential decay (MED), maximum attenuation (MA) and nonzero gradient (NZG) models show that on a site by site basis, the NZG model gives the best prediction of excess attenuation due to vegetation. The MA model has been found to be the worst of the three models. The studies have shown that the measurement site used to obtain the NZG model parameter values given in International Telecommunication Union (ITU) [2001] is influenced by metal lampposts and passing traffic, and thus was based on corrupted data. The results show that the leaf state, measurement geometry and vegetation density are more important factors influencing signal attenuation than tree species or leaf shape. Generally, the 11.6 GHz signal was attenuated much more than the 1.3 and 2 GHz signals by vegetation in-leaf, but the differences in attenuation were not significant in the out-of-leaf state. A successful excess attenuation model due to vegetation must consider the measurement geometry and vegetation descriptive parameters as well as any contributions from ground reflection and/ or diffraction over the top or round edges of the trees.

Wireless sensor network technology holds great promise for application in a wide range of areas, both to monitor and control a variety of systems. Whilst the use of sensors has found natural applications within the manufacturing sector, application in agriculture is still in its infancy and has been used largely to only monitor the environment. The use of technology in the agricultural sector to improve crop yield, quality and to foster sustainable agriculture can be regarded as one of the areas that will provide food security to the expanding global population and to mitigate food shortage precipitated by unpredictable weather patterns. This paper presents a Wireless Sensor Network coverage measurements in a mixed crop farming, modeling and deployment architecture taking into account the different signal propagation scenarios and attenuation factor of different crops. Most importantly, the paper presents wireless sensor network deployment architecture for a mixed crop trial field over an area of 54,432m 2 , which is 4% of the total area to be covered by the final network.

Abstract-As wireless communication moves from long to short ranges with considerably lower antenna heights, the need to understand and be able to predict the impact of vegetation on coverage and quality of wireless services has become very important. This paper focuses on vegetation attenuation measurements for frequencies in the range 0.4-7.2 GHz in mango and oil palm plantations to evaluate vegetation attenuation models for application in wireless sensor network planning and deployment in precision agriculture. Although a number of models have been proposed and evaluated for specific frequencies, results show that these models do not perform well when applied to different vegetation types or at different frequencies. A global assessment of the models using a broad range of frequencies shows that the COST 235 model gives more consistent results when there is vegetation in the propagation path. For grid-like plantation, the study shows that the RET model provides the best prediction of path loss for measurements between two rows of trees. However, taking into account the limited number of parameter values available for the RET model and the potential inaccuracy that may results from the use of a wrong parameter value, a sub-optimal model which combines the ITUR model with ground reflection does offer a more consistent prediction. The differences in the average values of RMS error between RET, ITUR and free space loss models when combined with ground reflection is less than 1.6 dB.

Purpose -The paper aims to propose a system that uses a combination of techniques to suggest weld requirements for ships parts. These suggestions are evaluated, decisions are made and then weld parameters are sent to a program generator. Design/methodology/approach -A pattern recognition system recognizes shipbuilding parts using shape contour information. Fourier-descriptors provide information and neural networks make decisions about shapes. Findings -The system has distinguished between various parts and programs have been generated so that the methods have proved to be valid approaches. Practical implications -The new system used a rudimentary curvature metric that measured Euclidean distance between two points in a window but the improved accuracy and ease of implementation can benefit other applications concerning curve approximation, node tracing, and image processing, but especially in identifying images of manufactured parts with distinct corners. Originality/value -A new proposed system has been presented that uses image processing techniques in combination with a computer-aided design model to provide information to a multi-intelligent decision module. This module will use different criteria to determine a best weld path. Once the weld path has been determined then the program generator and post-processor can be used to send a compatible program to the robot controller. The progress so far is described.