This paper presents a multistable discrete memristor that is based on the discretization of a continuous-time model. It has been observed that the discrete memristor model is capable of preserving the characteristics of the continuous memristor model. Furthermore, a three-dimensional memristor discrete-time FitzHugh–Nagumo model is constructed by integrating the discrete memristor into a two-dimensional FitzHugh–Nagumo (FN) neuron model. Subsequently, the dynamic behavior of the proposed neuron model is analyzed through Lyapunov exponents, phase portraits, and bifurcation diagrams. The results show multiple kinds of coexisting hidden attractor behaviors generated by this neuron model. The proposed approach is expected to have significant implications for the design of advanced neural networks and other computational systems, with potential applications in various fields, including robotics, control, and optimization.

A novel and low-cost framework for food traceability, composed by commercial and proprietary sensing devices, for the remote monitoring of air, water, soil parameters and herbicide contamination during the farming process, has been developed and verified in real crop environments. It offers an integrated approach to food traceability with embedded systems supervision, approaching the problem to testify the quality of the food product. Moreover, it fills the gap of missing low-cost systems for monitoring cropping environments and pesticides contamination, satisfying the wide interest of regulatory agencies and final customers for a sustainable farming. The novelty of the proposed monitoring framework lies in the realization and the adoption of a fully automated prototype for in situ glyphosate detection. This device consists of a custom-made and automated fluidic system which, leveraging on the Molecularly Imprinted Polymer (MIP) sensing technology, permits to detect unwanted glyphosate contamination. The custom electronic mainboard, called ElectroSense, exhibits both the potentiostatic read-out of the sensor and the fluidic control to accomplish continuous unattended measurements. The complementary monitored parameters from commercial sensing devices are: temperature, relative humidity, atmospheric pressure, volumetric water content, electrical conductivity of the soil, pH of the irrigation water, total Volatile Organic Compounds (VOCs) and equivalent CO${}_2$. The framework has been validated during the olive farming activity in an Italian company, proving its efficacy for food traceability. Finally, the system has been adopted in a different crop field where pesticides treatments are practiced. This has been done in order to prove its capability to perform first level detection of pesticide treatments. Good correlation results between chemical sensors signals and pesticides treatments are highlighted.

This paper presents a fast high-linearity low-power versatile interface circuit for MOX gas sensor. The device is based on a resistance-to-frequency converter with digital output, featuring 128dB-Dynamic Range with 8-bit accuracy. This is achieved with a constant measurement time of 250ms (i.e. with 4Hz output data-rate). The ASIC covers the 100Ω -1MΩ resistive range, with large programmability for the sensor resistance values. Large linearity (no glitches) is achieved with a single-scale measurement arrangement. The design effort reduces the power consumption to fit portable devices applications. The interface is realized in a standard CMOS 130nm technology with an area of 125000 &m2 and 450&A-Total Current from a single 1.5V supply. Electrical and chemical measurements with a SnO2 sensor validate the results, showing high minimum CO detection capability in ambient air of 5ppm.

Smart Breath Analyzers were developed as sensing terminals of a telemedicine architecture devoted to remote monitoring of patients suffering from Chronic Obstructive Pulmonary Disease (COPD) and home-assisted by non-invasive mechanical ventilation via respiratory face mask. The devices based on different sensors (CO₂/O₂ and Volatile Organic Compounds (VOCs), relative humidity and temperature (R.H. & T) sensors) monitor the breath air exhaled into the expiratory line of the bi-tube patient breathing circuit during a noninvasive ventilo-therapy session; the sensor raw signals are transmitted pseudonymized to National Health Service units by TCP/IP communication through a cloud remote platform. The work is a proof-of-concept of a sensors-based IoT system with the perspective to check continuously the effectiveness of therapy and/or any state of exacerbation of the disease requiring healthcare. Lab tests in controlled experimental conditions by a gas-mixing bench towards CO₂/O₂ concentrations and exhaled breath collected in a sampling bag were carried out to test the realized prototypes. The Smart Breath Analyzers were also tested in real conditions both on a healthy volunteer subject and a COPD suffering patient.

An energy-efficient, wide dynamic-range (DR) CMOS analog front-end (AFE) for chemiresistive sensors is presented. The circuit is specifically designed for the Metal Oxide (MOX) gas sensors, a special technology of chemiresistive sensors, broadly diffused in modern portable devices due to their low-cost and simplicity of use. Energy efficiency is mandatory for the AFE in order to prolong the battery life that supply these devices. The proposed circuit implements the resistance-to-time (R-to-T) conversion of the sensor's resistance by adopting a relaxation oscillator-based architecture. A limiting resistor in series with the sensor is introduced for reducing the circuit's energy-per-measurement (EpM), while mitigating the error due to the sensor's parasitic capacitance. The analysis of the circuit is presented with emphasis on the design trade-off between error due to the sensor's parasitic capacitance and power consumption on one side and read-out sensitivity on the other. The chip prototype is realized in AMS 0.35 μ m process and has been tested in the DR between 100Ω and 4.7MΩ with an accuracy less than 0.1% and a precision less than 0.029%. The efficacy of the presented AFE is proved by adopting the circuit in a real chemical environment with a commercial sensor. The proposed AFE shows a maximum EpM of 296nJ which is three times better than the state of the art.