This paper presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition and control system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy and to prevent false alarms, two gas sensor influence quantities, i.e., temperature and humidity, are also measured. Advanced processing based on multiple-input-single-output neural networks is implemented at the network sensing nodes to obtain temperature and humidity compensated gas concentration values. Anomalous operation of the network sensing nodes and power consumption are also discussed.

In this paper, a new four-electrode sensor for water conductivity measurements is presented. In addition to the sensor itself, all signal conditioning is implemented together with signal processing of the sensor outputs to determine the water conductivity. The sensor is designed for conductivity measurements in the range from 50 mS/m up to 5 S/m through the correct placement of the four electrodes inside the tube where the water flows. The implemented prototype is capable of supplying the sensor with the necessary current at the measurement frequency, acquiring the sine signals across the voltage electrodes of the sensor and across a sampling impedance to determine the current. A temperature sensor is also included in the system to measure the water temperature and, thus, compensate the water-conductivity temperature dependence. The main advantages of the proposed conductivity sensor include a wide measurement range, an intrinsic capability to minimize errors caused by fouling and polarization effects, and an automatic compensation of conductivity measurements caused by temperature variations.

There are no rules to select the best curve-fitting method for a given set of data. This problem is of great importance in measurement applications. Optimizing analog and digital methods for a transducer's characteristic interpolation or linearization is a field where constant research is being done, particularly since auto-calibration and self-test of intelligent transducers is a topic of major interest. We present an overview of classical methods for data interpolation and least mean squares regression. We make a comparative evaluation of the relative performance of polynomial and artificial neural networks approximations to measurement data with particular attention paid to the reduction of the required calibration set dimension to obtain a given accuracy.

Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information. When integrated with voice control mechanisms, gestures and even iris movement, it becomes a valuable tool for medicine. These features are particularly appealing for the operating room and surgical learning, where access to information and freedom of hand operation are fundamental. This study examines the most significant research on mixed reality in the operating room over the past five years, to identify the trends, use cases, its applications and limitations. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to answer the research questions established using the PICO (Population, Intervention, Comparator and Outcome) framework. Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes. The articles’ conclusions highlight the potential benefits of these innovations in surgical practice while acknowledging the challenges that must be addressed. Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The need for more flexible approaches and comprehensive studies is underscored by the specificity of procedures and limited samples sizes. The integration of imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

The challenges of climate change, population growth, demographic change, urbanization and resource depletion mean that the world's great cities need to adapt to survive and thrive over the coming decades. Slashing greenhouse gas emissions to prevent catastrophic climate change, while maintaining or increasing quality of life, can be a costly and difficult process. Two factors that directly affect the life quality in the XXI century cities are the water and air quality that can be monitored using the combination of low cost sensing modules, machine to machine (M2M) and internet of things (IoT) technologies. In this context, this study presents a wireless sensor network architecture that combines low cost sensing nodes and a low cost multi‐parameters sensing probe for reliable monitoring of water quality parameters of surface waters (lakes, estuaries and rivers) in urban areas. A particular attention is dedicated to the design of the conductivity, temperature and turbidity signal conditioning circuits, highlighting important issues related to linearisation, measuring dynamic range and low‐cost implementation by using commercial off‐the‐shelf components and devices. Several issues related to the wireless sensor network implementation are included in this study, as well as several simulation and experimental results.