Hesham Hassan

Airborne LiDAR scanning systems are one of the most advanced remote sensing systems. They are capable to rapidly cover large geographical areas to gather data from with very high precision and great density. As a result, obtained datasets can contain tens of millions of points which consume more than a gigabyte per square kilometers (GB/km2). In practice, significant problems had been issued such as expensive storage, difficult distribution to the users, and time-consuming exchange over the internet and data processing and display time. For these reasons, LiDAR data compression has become recently a critical issue. In this paper, we compared three LiDAR domain-specific compression algorithms (LASzip, LASComp, and LiDAR Compressor) against three general-purpose compression algorithms (7-Zip, WinZip, and WinRAR). We have used real Airborne LiDAR point clouds data for Washington, DC (District of Columbia) for doing the experiments to reflect real LiDAR data compression issues. In this work, the algorithms had been evaluated in terms of Compression Ratios, Compression Times, and Bits per Point. Also, we have evaluated effects of point cloud density and number of contained points on the compression efficiency. Experiment results indicated that LASzip algorithm outperforms other algorithms with average compression ratio achieved 16.63% and average compression time achieved 16.65 sec. on the other hand, the general-purpose compression algorithm (WinRAR) surpass the LiDAR domain-specific compression algorithm (LiDAR Compressor) with compression ratio achieved 20.24%.

Common knowledge acquisition and documentation structuring (CommonKADS) methodology is used for building knowledge-based systems. Legacy systems built depending on CommonKADS suffer from weak points regarding reusability. The main objectives of this work are: (1) switching of CommonKADS methodology from just a design model to be an executable application, (2) facilitating the linkage and cooperation between CommonKADS services that are using different terminologies and (3) enhancing suitability and reusability of existing CommonKADS-based systems. An enhancement to the CommonKADS methodology in order to improve its applications reusability is introduced. This enhancement contains an adaptation of the original CommonKADS methodology and utilization of service-oriented architecture (SOA) as a promising software engineering technology. The proposed adaptation model includes two alternative processes: adjusting and converting. An adjusting process performs a transformation of the existing CommonKADS layers to be SOA-enabled so that it includes representing the data in a new standard form to be transformed into set of services. Converting process is concerned with changing CommonKADS-based legacy systems to SOA-enabled systems through using GenericSOA framework. An example application, a potato CommonKADS-based expert system, was used to evaluate the new proposed model through the analysis and automated restructuring of it to be SOA-enabled system.

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Document analysis is an important research field that aims to gather information by analyzing data in documents. As one of the important targets for many fields is to understand what people actually want, sentimental analysis field has been one of the vital fields that are tightly related to the document analysis. This research focuses on analyzing text documents to classify each document according to its opinion. The aim of this research is to detect the emotions from text documents based on enhancing the lexicon based approach. The proposed approach has been presented and different experiments are applied by different perspectives to reveal the positive impact of the proposed approach on the classification results.