This work describes the development of a teaching strategy to leverage current simulation tools and promote learning of industrial automation systems. Specifically, Programmable Logic Controller (PLC) programming in an industrial automation course. We propose an infrastructure where it is possible to work with physical, virtual and mixed laboratories

Active filter design is a mature topic that provides good solutions that can be implemented using discrete devices or integrated circuit technology. For instance, when the filter topologies are implemented using commercially available operational amplifiers (opamps), one can explore varying circuit parameters to tune the central frequency or enhance the quality (Q) factor. We show the addition of a feedback loop in the signal flow graph of a biquadratic filter topology, which enhances Q and highlights that a sensitivity analysis can be performed to identify which circuit elements influence central frequency, Q, or both. In this manner, we show the opamp-based implementation of a biquadratic bandpass filter, in which Q is enhanced through performing a sensitivity analysis for each circuit element. Equations for the central frequency and Q are provided to observe that there is not a direct parameter that enhances them, but we show that from sensitivity analysis one can identify the circuit elements that better enhance Q-factor.

This work presents a reconfigurable-band analog filter capable of performing the low-pass, high-pass, band-pass or all-pass functions. The proposed filter is a simple and compact cell composed of only two RC networks and one differential Operational Transconductance Amplifier (OTA). To select the desired filter type, two configurations must be done: 1) the two RC network shall be configured as low-pass/low-pass, high-pass/high-pass or low-pass/high-pass, 2) the input signals must be fed in differential or single mode. To validate theory and simulations, the proposed reconfigurable-band filter was designed, fabricated and measured to meet the biomedical band (0.1 Hz - 10 kHz) in a $0.5 ~\mu \text{m}$ CMOS standard fabrication process. To achieve the extremely low RC time constants imposed by the biomedical band, the R element was implemented using PMOS high-value programmable resistor. The prototype exhibits a silicon area of $470 ~\mu \text{m}$ x $250 ~\mu \text{m}$ and a power consumption of $96.32 ~\mu \text{W}$ using a ± 0.8 V symmetric power supply. Due to this circuit’s ability to perform four filter-functions, its operation band can be tuned over five decades, its power consumption is in the nano and micro-watt regime, and it is area compact, the proposed analog filter is a suitable candidate for being an analog standard-cell.

In this paper the results of using a Multi-agent system (MAS) for Structural Health Monitoring (SHM) are detailed. A study between different MAS architectures reported in literature is presented in order to select and adapt the most adequate one for SHM tasks. Requirements are established according to recent solutions, where main parameters are type and number of sensors and communication protocols, among others. MAS technique uses several intelligent agents, that are algorithms able to act in a reactive or active way. Their action depends on surrounding environment or collected data. These agents can work in a decentralized way, searching the fulfillment of an individual goal or they can work with another system to achieve a common goal. Decision is based on their internal state (beliefs, goals and commitments). MAS’ effectiveness depends on the interconnection between the agents. Type of agents is defined according to its communication method and protocol, common and individual goals, among others. Decentralization and versatility are two important characteristics of MAS technique useful to solve SHM problem. This is one of the main motivations to consider this technique to be a good approach for the studied problem. A benchmark numerical model, which consists of a metallic framework, was used to validate and demonstrate the feasibility of the selected architecture for SHM