Water is a vital source for life and natural environments. This is the reason why water sources should be constantly monitored in order to detect any pollutants that might jeopardize the quality of water. This paper presents a low-cost internet-of-things system that is capable of measuring and reporting the quality of different water sources. It comprises the following components: Arduino UNO board, Bluetooth module BT04, temperature sensor DS18B20, pH sensor—SEN0161, TDS sensor—SEN0244, turbidity sensor—SKU SEN0189. The system will be controlled and managed from a mobile application, which will monitor the actual status of water sources. We propose to monitor and evaluate the quality of water from five different water sources in a rural settlement. The results show that most of the water sources we have monitored are proper for consumption, with a single exception where the TDS values are not within proper limits, as they outperform the maximum accepted value of 500 ppm.

This paper presents gate level delay dependent probabilistic fault models for CMOS circuits operating at sub-threshold and near-threshold supply voltages. A bottom-up approach has been employed: SPICE simulations have been used to derive higher level error models implemented using Verilog HDL. HSPICE Monte-Carlo simulations show that the delay dependent probabilistic nature of these faults is due to the process-voltage-temperature (PVT) variations which affect the circuits operating at very low supply voltages. For gate level error analysis, mutant based simulated fault injection (SFI) techniques have been employed for combinational net list reliability analysis. Four types of gate level fault models, with different accuracies, are proposed. Our findings show that the proposed SFI method presents a 2X-5X simulation time overhead compared to the simulation of the gold circuit, with respect to SPICE analysis, the proposed method requires three orders of magnitude less simulation time.

This paper proposes a solution for guiding visually impaired people to reach predefined locations marked with preregistered passive ultra-high-frequency RFID tags inside public buildings (e.g., secretary’s offices and information desks). Our approach employs an unmanned ground vehicle guidance system that assists customers in following predefined routes. The solution also includes a methodology for recording the best routes between all possible locations that may be visited. When reaching the destination, the system will read the tag, extract all the associated information from a database, and translate it into an audio format played into the user’s headphones. The system includes functionalities such as recording and playback of prerecorded routes, voice commands, and audio instructions. By describing the software and hardware architecture of the proposed guiding systems prototype, we show how combining ultra-high-frequency RFID technology with unmanned ground vehicle guiding systems equipped with ultrasonic, grayscale, hall sensors, and voice interfaces allows the development of accessible, low-cost guiding systems with increased functionalities. Moreover, we compare and analyze two different modes of route recording based on line following and manual recording, obtaining a performance regarding route playback with deviations under 10% for several basic scenarios.

The system consists of an energetically and informationally autonomous node, which collects data about atmospheric pressure, temperature, humidity, and gas concentrations for carbon monoxide and methane and sends this data to a dedicated receiver that will store it and make it available for visualization. Communication is realized through the IEEE 802.15.1 standard, via Bluetooth. The node consists of an Arduino Nano which uses an Atmega328P microcontroller, a sensor BMP180 module, two modules for detecting gases - MQ-7 for carbon monoxide and MQ-4 for methane, an HR202 module for humidity and an HC-05 Bluetooth module. On the receiving end, a mobile application for Android phones developed using Android Studio and Java is used. The data can be then visualized as graphs using a Python script. The system can be used to monitor the temperature, humidity, and atmospheric pressure of a room as well as add a layer of safety with alerts when the danger threshold for methane or CO is passed.

Emotion monitoring technologies leveraging detection of facial expressions have gained important attention in psychological and social research due to their ability of providing objective emotional measurements. However, this paper addresses a gap in the literature consisting of the correlation between emotional facial response and sentiment analysis of written texts, developing a system capable of recognizing real-time emotional responses. The system uses a Raspberry Pi 4 and a Pi Camera module in order to perform real-time video capturing and facial expression analysis with the DeepFace version 0.0.80 model, while sentiment analysis of texts was performed utilizing Afinn version 0.1.0. User secure authentication and real-time database were implemented with Firebase. Although suitable for assessing psycho-emotional health in test takers, the system also provides valuable insights into the strong compatibility of the sentiment analysis performed on texts and the monitored facial emotional response, computing for each testing session the “compatibility” parameter. The framework provides an example of a new methodology for performing comparisons between different machine learning models, contributing to the enhancement of machine learning models’ efficiency and accuracy.