Active and passive realization of Fractance device of order1/2is presented. The crucial point in the realization of fractance device is finding the rational approximation of its impedance function. In this paper, rational approximation is obtained by using continued fraction expansion. The rational approximation thus obtained is synthesized as a ladder network. The results obtained have shown considerable improvement over the previous techniques.

This paper deals with the design and applications of digital differentiators. For real time applications it is mandatory that a differentiator should have as small an order as possible. Different procedures available for the design of FIR and IIR type digital differentiators are presented. The IIR Type digital differentiators are obtained by inversion and magnitude stabilization of the existing digital integrators. In some applications like controls, waveshaping, oscillators and communications require a constant 90° phase for differentiators. In this paper an attempt is made to study about the variation of phase angle of digital differentiators with the application of fractional delay. The use of digital differentiators for the detection of edges in an image, QRS detection in an ECG signal is illustrated. It has been observed that the digital differentiators have shown superior performance compared to the well known gradient method. With the proven efficiency of the differentiators in various applications, they have been implemented in hardware using Verilog.

In machine learning, Classification is one of the most important research area. Classification allocates the given input to a known category. In this paper different machine algorithms like Logistic regression (LR), Decision tree (DT), Support vector machine (SVM), K nearest neighbors (KNN) were implemented on UCI breast cancer dataset with preprocessing. The models were trained and tested with k-fold cross validation data. Accuracy and run time execution of each classifier are implemented in python.