An automatic method is proposed to design CMOS compatible voltage followers (VFs) by applying genetic algorithms. It is described how an automatic system can deals with huge search spaces to design practical VFs by performing evolutionary operations from nullator-based descriptions. The proposed method consists of three main steps: generation of the small-signal circuitry, addition of biases, and sizing by using standard CMOS technology of 0.35µm. Furthermore, it is described how to synthesize VFs by codifying the three main steps into three kinds of genes, and how to select small-signal, biased, and sized topologies to generate potential solutions. Finally, several applications are discussed along with the evolution of VFs to design current conveyors.
A fuzzy sets intersection procedure to select the optimum sizes of analog circuits composed of metal-oxidesemiconductor field-effect-transistors (MOSFETs), is presented. The cases of study are voltage followers (VFs) and a current-feedback operational amplifier (CFOA), where the width (W) and length (L) of the MOSFETs are selected from the space of feasible solutions computed by swarm or evolutionary algorithms. The evaluation of three objectives, namely: gain, bandwidth and power consumption; is performed using HSPICETM with standard integrated circuit (IC) technology of 0.35μm for the VFs and 180nm for the CFOA. Therefore, the intersection procedure among three fuzzy sets representing “gain close to unity”, ”high bandwidth” and “minimum power consumption”, is presented. The main advantage relies on its usefulness to select feasible W/L sizes automatically but by considering deviation percentages from the desired target specifications. Basically, assigning a threshold to each fuzzy set does it. As a result, the proposed approach selects the best feasible sizes solutions to guarantee and to enhance the performances of the ICs in analog signal processing applications.
Oil and gas industry, worldwide, needs to monitor, control and assess the elements that are involved in the general oil transportation and production processes. However, these processes are not risk free. The project proposes an intelligent support system that provides optimized projections for effective risk management. The project focuses on the development of a set of Genetic Algorithms (GAs), a branch of AI systems that assists to optimize the usage and distribution of resources. GAs will reduce the latent risks and potential dangers as much as possible. The main purpose is to minimize the risk levels in a pipeline segment based on their condition and by detecting optimal variable configurations: their Risk of Failure (RoF), Probability of Failure (PoF), Consequence of Failure (CoF), and their sub elements (threats and impacts). The heuristic results generated by this set of GAs show a significant reduction on the risk assessment measures, by finding “optimized” configurations of these variables.
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