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Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

Binary Decision Diagrams (BDDs) are very useful structures to represent Boolean function in VLSI synthesis. Time taken to build a BDD and obtaining its size plays a major role in the time of complexity of VLSI synthesis. This time complexity increases drastically as the number of input variables increases. Various models to estimate the size of the BDD, without actually building it already exists. These models claim to support both simplified and un-simplified Boolean functions. The models were developed under the justification that time to estimate will be far less compared to the time taken to actually build the BDD. There are two drawbacks with the existing model. First drawback is that, the current model just follows a random curve fit without any substantial mathematical support. Second drawback is the existing model is based on experimental results which used only less than ten variables. Since current practical functions may use hundreds of variables, there is no guarantee that the model is accurate enough. Given the two drawbacks, it becomes necessary to test the existing model for more complex circuits with hundreds of variables. In this paper the existing models were tested with standard benchmark circuits. Results were compared with actual BDD sizes of the benchmarks and the estimated sizes from the parameters of the benchmarks. Comparison of the results proved that existing models give poor results for the circuits with more than ten variables and existing models become inapplicable to most of the current practical functions that uses more than hundreds of variables.

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