Floating-gate MOS transistors have been widely used in diverse analog and digital applications. One of these is as a charge sensitive device in sensors for pH measurement in solutions or using gates with metals like Pd or Pt for hydrogen sensing. Efforts are being made to monolithically integrate sensors together with controlling and signal processing electronics using standard technologies. This can be achieved with the demonstrated compatibility between available CMOS technology and MEMS technology. In this paper an in-depth analysis is done regarding the reliability of floating-gate MOS transistors when charge produced by a chemical reaction between metallic oxide thin films with either reducing or oxidizing gases is present. These chemical reactions need temperatures around 200 °C or higher to take place, so thermal insulation of the sensing area must be assured for appropriate operation of the electronics at room temperature. The operation principle of the proposal here presented is confirmed by connecting the gate of a conventional MOS transistor in series with a Fe₂O₃ layer. It is shown that an electrochemical potential is present on the ferrite layer when reacting with propane.

As soft-hardware-logic circuits had been proposed in the literature as an alternative for digital circuits taking advantage the fact that any Boolean function could be implemented with the same cell, just configuring external signals, this work shows a methodology that could be followed particularly for the design of a four bits logic gate, using the so-called neuron MOS transistor (ν-MOS). Simulation results show the feasibility of the design for performing as XNOR, NOR, OR, XOR, AND or NAND logic gates, for instance. In order to extrapolate the design to a higher number of bits, the key issue is to properly consider the weight of the input capacitances in correlation with the number of input bits. A D/A converter can be used as the input stage of the configuration. This design considers the D/A converter-less version, since it helps to increase device integration as the number of transistors used is reduced with no difference in its performance. The design should be based on the theoretical floating potential diagram (FPD) of the desired logic gate.

This paper introduces a proposal for a wireless network of smart sensors designed to capture the vibrations produced by the impact of a person’s footsteps on a dispersive and damped floor. These vibrations are utilized to pinpoint the location of impacts using a heuristic algorithm, enabling operation at a low data transmission/reception rate. Traditionally, the placement of sensors within a room has not been given significant consideration in the context of localization; however, our findings indicate that a detailed analysis of both the number and location of sensors can substantially enhance the accuracy of footstep localization. In this study, we have optimized the arrangement and quantity of sensors through the application of bio-inspired metaheuristics, aiming to minimize localization errors across the room. Upon evaluating various bio-inspired metaheuristic optimization algorithms, we identified the one that yielded the lowest estimation errors for the room as a whole. Our experimental tests demonstrate that such optimization significantly enhances the efficacy of the localization algorithm, resulting in a reduction of localization error ranging from 18.24% to 46.78% across different trajectories.

A system designed for monitoring the footsteps of a person is presented, aimed at determining characteristic and statistical parameters of the individual’s gait. This non-invasive approach utilizes a low-cost commercial capacitive accelerometer to sense the vibrations caused by each step as an individual walks on the floor. The system captures signals from the accelerometer, which are then processed to obtain different signal parameters (such as step duration, cadence, stride duration, kurtosis, skewness, etc), providing information about each subject under study. The collected information is stored in a database, and artificial neural networks are employed in this report to classify types or styles of walking, as well as to identify the person’s gender, age, and body mass index. With the implementation of classifiers, physical characteristics can be grouped, potentially focusing on diagnoses or identifications based on specific data. Finally, the results obtained from tests performed on 30 volunteers are presented, verifying the accelerometer’s performance and the algorithm’s effectiveness, with accuracy percentages up to 99.2% for classification. The results show a high level of coincidence and are promising for the future improvement of the system.