Miniaturized electrochemical pH sensors are increasingly in demand for applications such as online monitoring of water quality and health monitoring. The metal oxides are the best candidates for sensing electrodes of such sensors as they offer high chemical stability. In this paper, we present a novel approach to obtain interdigitated conductimetric pH sensor using screen printing of TiO ₂ thick film on an alumina substrate. The microstructural and crystalline properties of the TiO ₂ sensitive film were examined with scanning electron microscopy and Raman spectroscopy. The impedance spectroscopic studies of the fabricated thick film sensor were carried out in the frequency range of 5-20 kHz for the test solutions in the pH range of 4-10 and it was observed that the impedance of the film is distinctly dependent on pH. Using the measured impedance data, we have also proposed an equivalent RC network model for the fabricated pH sensor. The physical meaning of the model parameters was determined by electrochemical impedance spectroscopic analysis, and through statistical analysis it was found that all parameters are distinctly pH-dependent.

This paper describes realization of complex impedance measurement system based on integrated circuit AD5933. AD5933 is controlled by ATmega128 which performs all calculations and creates report for off-line analysis on PC. Created report of measured values of impedance magnitude and phase angle is stored on micro SD card in format which is compatible with MS Excel. Realized device is equipped with self-calibration system which ensures high accuracy in wide impedance range. Device also has alphanumeric LCD for displaying results. Realized system can be used for sensor or complex impedance measurements, structural health monitoring, noninvasive blood impedance measurements, electro-impedance spectroscopy, etc.

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.

In recent years, advancements in technology are constantly driving the miniaturization of electronic devices, not only in the renowned domain of Internet-of-Things but also in other fields such as that of flexible and textile electronics. As the latter forms a great ecosystem for new devices, that could be functional such as heating garments or sensory, many suppliers have already started producing and bringing to market conductive threads that can be used by researchers and the mass public for their work. However, to date, no extensive characterization has been carried out with respect to the electrical performance of such threads and that is what this article is aiming to amend. Four commercially available threads by two different suppliers were put under test, to establish their limitations in terms of maximum power handling, both continuous and instantaneous. They were subsequently examined at a microscopic scale as well, to verify any potential caveats in their design, and any hidden limitations. A preliminary profile for each of the four threads was successfully established.

Anomaly-based intrusion detection systems identify the computer network behavior which deviates from the statistical model of typical network behavior. Binary classifiers based on supervised machine learning are very accurate at classifying network data into two categories: normal traffic and anomalous activity. Most problems with supervised learning are related to the large amount of data required to train the classifiers. Feature selection can be used to reduce datasets. The goal of feature selection is to select a subset of relevant input features to optimize the evaluation and improve performance of a given classifier. Feature scaling normalizes all features to the same range, preventing the large size of features from affecting classification models or other features. The most commonly used supervised machine learning models, including decision trees, support vector machine, k-nearest neighbors, weighted k-nearest neighbors and feedforward neural network, can all be improved by using feature selection and feature scaling. This paper introduces a new feature scaling technique based on a hyperbolic tangent function and damping strategy of the Levenberg–Marquardt algorithm.