Multi-Sensor Device for Traceable Monitoring of Indoor Environmental Quality.

The Indoor Environmental Quality (IEQ) combines thermal, visual, acoustic, and air-quality conditions in indoor environments and affects occupants' health, well-being, and comfort. Performing continuous monitoring to assess IEQ is increasingly proving to be important, also due to the large amount of time that people spend in closed spaces. In the present study, the design, development, and metrological characterization of a low-cost multi-sensor device is presented. The device is part of a wider system, hereafter referred to as PROMET&O (PROactive Monitoring for indoor EnvironmenTal quality & cOmfort), that also includes a questionnaire for the collection of occupants' feedback on comfort perception and a dashboard to show end users all monitored data. The PROMET&O multi-sensor monitors the quality conditions of indoor environments thanks to a set of low-cost sensors that measure air temperature, relative humidity, illuminance, sound pressure level, carbon monoxide, carbon dioxide, nitrogen dioxide, particulate matter, volatile organic compounds, and formaldehyde. The device architecture is described, and the design criteria related to measurement requirements are highlighted. Particular attention is paid to the calibration of the device to ensure the metrological traceability of the measurements. Calibration procedures, based on the comparison to reference standards and following commonly employed or ad hoc developed technical procedures, were defined and applied to the bare sensors of air temperature and relative humidity, carbon dioxide, illuminance, sound pressure level, particulate matter, and formaldehyde. The next calibration phase in the laboratory will be aimed at analyzing the mutual influences of the assembled multi-sensor hardware components and refining the calibration functions.

A More General Quantum Credit Risk Analysis Framework.

Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset's default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector.

A Novel Redundant Validation IoT System for Affective Learning Based on Facial Expressions and Biological Signals.

Teaching is an activity that requires understanding the class's reaction to evaluate the teaching methodology effectiveness. This operation can be easy to achieve in small classrooms, while it may be challenging to do in classes of 50 or more students. This paper proposes a novel Internet of Things (IoT) system to aid teachers in their work based on the redundant use of non-invasive techniques such as facial expression recognition and physiological data analysis. Facial expression recognition is performed using a Convolutional Neural Network (CNN), while physiological data are obtained via Photoplethysmography (PPG). By recurring to Russel's model, we grouped the most important Ekman's facial expressions recognized by CNN into active and passive. Then, operations such as thresholding and windowing were performed to make it possible to compare and analyze the results from both sources. Using a window size of 100 samples, both sources have detected a level of attention of about 55.5% for the in-presence lectures tests. By comparing results coming from in-presence and pre-recorded remote lectures, it is possible to note that, thanks to validation with physiological data, facial expressions alone seem useful in determining students' level of attention for in-presence lectures.

An Investigation on Pervasive Technologies for IoT-based Thermal Monitoring.

Indoor thermal monitoring is a crucial requirement for home automation, which fits inside the ever-growing Internet of Things (IoT) paradigm. The IoT ecosystem aims at connecting every device exploiting specific functions, deployed in a particular place, in order to give the chance to the users to monitor and/or control some aspects of their life, or to demand this task to a proper software. In the thermal monitoring context, IoT provides new opportunities for a dense and/or large-scale distribution of sensors, which have to gather data in order to effectively control the Heating, Ventilation and Air Conditioning (HVAC) system. Several wireless technologies can be exploited for this scope. However, they involve different benefits and drawbacks. In particular, this study is focused on Radio Frequency Identification (RFID) and Bluetooth®, which represent two well-known wireless technological standards used by commercial electronics but suitable also for pervasive IoT systems. These technologies are discussed and compared from several points of view, i.e., flexibility, reliability, battery life and cost of the system. A theoretical analysis highlights their benefits for the application context and evaluates their suitability to dense and large-scale monitoring systems. The theoretical results are supported by an experimental analysis based on the implementation and test of two different systems, one using RFID and the other using Bluetooth technology.

Nonlinear Predictive Threshold Model for Real-Time Abnormal Gait Detection.

Falls are critical events for human health due to the associated risk of physical and psychological injuries. Several fall-related systems have been developed in order to reduce injuries. Among them, fall-risk prediction systems are one of the most promising approaches, as they strive to predict a fall before its occurrence. A category of fall-risk prediction systems evaluates balance and muscle strength through some clinical functional assessment tests, while other prediction systems investigate the recognition of abnormal gait patterns to predict a fall in real time. The main contribution of this paper is a nonlinear model of user gait in combination with a threshold-based classification in order to recognize abnormal gait patterns with low complexity and high accuracy. In addition, a dataset with realistic parameters is prepared to simulate abnormal walks and to evaluate fall prediction methods. The accelerometer and gyroscope sensors available in a smartphone have been exploited to create the dataset. The proposed approach has been implemented and compared with the state-of-the-art approaches showing that it is able to predict an abnormal walk with a higher accuracy (93.5%) and a higher efficiency (up to 3.5 faster) than other feasible approaches.

A Mobile and Low-Cost System for Environmental Monitoring: A Case Study.

Northern Italy has one of the highest air pollution levels in the European Union. This paper describes a mobile wireless sensor network system intended to complement the already existing official air quality monitoring systems of the metropolitan town of Torino. The system is characterized by a high portability and low cost, in both acquisition and maintenance. The high portability of the system aims to improve the spatial distribution and resolution of the measurements from the official static monitoring stations. Commercial PM 10 and O 3 sensors were incorporated into the system and were subsequently tested in a controlled environment and in the field. The test in the field, performed in collaboration with the local environmental agency, revealed that the sensors can provide accurate data if properly calibrated and maintained. Further tests were carried out by mounting the system on bicycles in order to increase their mobility.

Toner Savings Based on Quasi-Random Sequences and a Perceptual Study for Green Printing.

Toner savings in monochromatic printing are an important target for improving green computing performance and more specifically green printing. In order to extend the lifetime of the printer cartridge, some options are available for laser printers, usually reducing the number of dots with respect to the normal print quality. However available algorithms and patents do not provide a method for dynamically adapting the percentage of toner savings to the required printing quality. In this paper, we introduce a new quasi-random sequence-based algorithm for reducing the number of dots in the printing process, able to achieve optimal discrepancy and low computational complexity, for all print quality levels. In order to reduce patterns in the removed dots, blue noise dithering is applied when the desired percentage of toner savings is moderate. The proposed solution can be easily implemented in the printer firmware, given its low computational complexity. In order to verify the results from a perceptual point of view, an extended test with 135 volunteers and more than 5000 comparisons has been performed, besides checking that toner is effectively saved. Results show that the proposed approach can produce a reduction of the perceived quality almost directly proportional to the number of monochromatic dots skipped, with only a reduced influence from the font used. The perceptual results are better in the proposal than in the previous approaches. The proposed algorithm appears to be a promising technique for improving green printing in monochromatic laser printers without using custom fonts.

Constructal thermodynamics combined with infrared experiments to evaluate temperature differences in cells.

The aim of this work was to evaluate differences in energy flows between normal and immortalized cells when these distinct biological systems are exposed to environmental stimulation. These differences were considered using a constructal thermodynamic approach, and were subsequently verified experimentally. The application of constructal law to cell analysis led to the conclusion that temperature differences between cells with distinct behaviour can be amplified by interaction between cells and external fields. Experimental validation of the principle was carried out on two cellular models exposed to electromagnetic fields. By infrared thermography we were able to assess small changes in heat dissipation measured as a variation in cell internal energy. The experimental data thus obtained are in agreement with the theoretical calculation, because they show a different thermal dispersion pattern when normal and immortalized cells are exposed to electromagnetic fields. By using two methods that support and validate each other, we have demonstrated that the cell/environment interaction can be exploited to enhance cell behavior differences, in particular heat dissipation. We propose infrared thermography as a technique effective in discriminating distinct patterns of thermal dispersion and therefore able to distinguish a normal phenotype from a transformed one.

Tracking endothelial cells using multiframe point correspondence.

This paper investigates the problem of tracking individual circular cell nuclei of endothelial cells during their migration. The purpose is to extract statistical information to support research activities devoted to reaching a better understanding of the underlying biological phenomena. This goal is achieved by exploiting a general tracking framework based on a multiframe point correspondence approach and capable of translating the matching problem into a maximum path cover problem over a graph based representation of moving objects. The application to phase contrast microscopy images containing hundreds of cells aggregating to form capillary tubes required to design an ad-hoc weight function capable of properly handling cells occlusion and aggregation.