Reconciling Synthesis and Decomposition: A Composite Approach to Capability Identification

Stakeholders' expectations and technology constantly evolve during the lengthy development cycles of a large-scale computer based system. Consequently, the traditional approach of baselining requirements results in an unsatisfactory system because it is ill-equipped to accommodate such change. In contrast, systems constructed on the basis of Capabilities are more change-tolerant; Capabilities are functional abstractions that are neither as amorphous as user needs nor as rigid as system requirements. Alternatively, Capabilities are aggregates that capture desired functionality from the users' needs, and are designed to exhibit desirable software engineering characteristics of high cohesion, low coupling and optimum abstraction levels. To formulate these functional abstractions we develop and investigate two algorithms for Capability identification: Synthesis and Decomposition. The synthesis algorithm aggregates detailed rudimentary elements of the system to form Capabilities. In contrast, the decomposition algorithm determines Capabilities by recursively partitioning the overall mission of the system into more detailed entities. Empirical analysis on a small computer based library system reveals that neither approach is sufficient by itself. However, a composite algorithm based on a complementary approach reconciling the two polar perspectives results in a more feasible set of Capabilities. In particular, the composite algorithm formulates Capabilities using the cohesion and coupling measures as defined by the decomposition algorithm and the abstraction level as determined by the synthesis algorithm.Comment: This paper appears in the 14th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS); 10 pages, 9 figure

Hybrid statistical-textural and intensity feature-based approach for accurate discrimination of retinal diseases

Retinal imaging features such as fundus lesions are crucial signs of several ocular diseases, such as diabetic retinopathy and age-related macular degeneration. The early and accurate detection of the aforementioned abnormalities is essential for correct diagnosis and therapeutic management. In this study, we propose a new hybrid statistical-textural and intensity feature-based method for the improvement of the discrimination between retinal diseases with machine learning classifiers. By incorporating statistical texture analysis to measure changes in pixel intensity, this method not only extracts features based on intensities of fundus images but also serves as the comparison tool for a complete evaluation of the retina. A robust feature set is developed by merging intensity-based features with several texture descriptors including GLCM, LBP etc. We extract these features and then use them to train and test the performance of a number of classifiers, such as Support Vector Machines (SVM) and Random Forests for more accurate detection. Experiments have been conducted on a public dataset of retinal images and with the support vector machine classifier, encouraging results compared to purely texture-based or intensity-based methods in terms of classification accuracy are obtained. The overall findings of this study underscore the promise of integrating both statistical-textural and intensity features for improved retinal disease detection process, which will serve as an important stepping stone for future investigations and production of automated screening tools in retinal. The method also holds potential to enable early intervention in the development of vision-related diseases and, ultimately, decrease their impact

Slow cooling of hot rolled bars to eliminate hydrogen induced cracks in Cr-Mo steels

283-287Hydrogen shows a high diffusivity in the solid phase and tends to escape during cooling, which reduces its concentration in steel. Low concentration of hydrogen is advantageous as it prevents hydrogen induced cracks. To ensure maximum removal of diffusible hydrogen, slow cooling of steel bars is investigated at JSW Steel Ltd, Salem Works (JSWSL). The hot rolled bars of Cr-Mo steel blooms such as 42CrMo4 and SCM 440H are slow cooled in a thermally insulated mild steel box. The effect of slow cooling on the microstructure, hardness and ultrasonic testing of bars are studied. The present paper discusses the results of slow cooling experiments conducted with Cr-Mo steel blooms and compares the properties of slow cooled and air-cooled bars. The slow cooled bars exhibited reduced hardness up to 78 BHN and are found to be free from ultrasonic defects when compared with air-cooled bars