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The goal of this paper is to detail the Analog Filter Netlist to Digital Filter Statements approach used to obtain the digital filter from the netlist of the analog filter. This method is done by replacing each component by its corresponding companion model and then discretizing the set of equations which describe in the time domain the behavior of all the circuit's components. Finally, the software implementation of the digital filter consists in the transcription of the discrete-time system of equations. We show that the selection of step-size might be sensitive problem for this approach. [ABSTRACT FROM AUTHOR]

This paper describes the procedure and the software process to verify the feasibility of a system in package. The process has been created to guarantee and ensure the main characteristics of such devices: a minimum time-to-market and great flexibility. A procedure is described for every step, starting from application definition to final electrical analysis. After a general introduction, all process design steps are technically analyzed; then each step of the final process check is described. Some comments and wishes are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Resolving circuit diagrams is a regular part of learning for school and university students from engineering backgrounds. Simulating circuits is usually done manually by creating circuit diagrams on circuit tools, which is a time-consuming and tedious process. We propose an innovative method of simulating circuits from hand-drawn diagrams using smartphones through an image recognition system. This method allows students to use their smartphones to capture images instead of creating circuit diagrams before simulation. Our contribution lies in building a circuit recognition system using a deep learning capsule networks algorithm. The developed system receives an image captured by a smartphone that undergoes preprocessing, region proposal, classification, and node detection to get a Netlist and exports it to a circuit simulator program for simulation. We aim to improve engineering education using smartphones by (1) achieving higher accuracy using less training data with capsule networks and (2) developing a comprehensive system that captures hand-drawn circuit diagrams and produces circuit simulation results. We use 400 samples per class and report an accuracy of 96% for stratified 5-fold cross-validation. Through testing, we identify the optimum distance for taking circuit images to be 10 to 20 cm. Our proposed model can identify components of different scales and rotations. [ABSTRACT FROM AUTHOR]