Abstract

Abstract
Reducing CO(2 )emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO(2 )emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO(2 )emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions.

Abstract
Falls are one of the causes of severe hilliness among elders, and the COVID-19 pandemic increased the number of unattended cases because of the social distancing measures. This study aims to create a dataset that collects the data from a 3-axis acceleration sensor fixed on a hinged board apparatus that mimics a human fall event. The datalogging system uses off-the-shelf devices to measure, collect and store the data. The resulting dataset includes data from different angle positions and heights, corresponding to joints of the lower limbs of the human body (ankle, knee, and hip). We use the dataset with a threshold-based fall detection algorithm. The result from the Receiver Operating Characteristic curve shows a good behavior with a mean Area Under the Curve of 0.77 and allow to compute a best threshold value with False Positive Rate of 14.8% and True Positive rate of 89.1%. The optimal threshold value may vary depending on the specific population, activity patterns, and environmental conditions, which may require further customization and validation in real-world settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
The use of wireless communications systems on the factory shop floor is becoming an appealing solution with many advantages compared to cable-based solutions, including low cost, easy deployment, and flexibility. This, combined with the continuous growth of low-cost mobile devices, creates opportunities to develop innovative and powerful applications that, in many cases, rely on computing and memory-intensive algorithms and low-latency requirements. However, as the density of connected wireless devices increases, the spectral noise density rises, and, consequently, the radio interference between radio devices increase. In this paper, we discuss how the density of AR/VR mobile applications with high throughput and low latency affect industrial environments where other wireless devices use the same frequency channel. We also discuss how the growing number of these applications may have an impact on the radio interference of wireless networks. We present an agnostic methodology to assess the radio interferences between wireless communication systems on the factory floor by using appropriate radio and system models. Several interference scenarios are simulated between commonly used radio systems: Bluetooth, Wi-Fi, and WirelessHART, using SEAMCAT. For a 1% probability of interference and considering a criterion of C/I = 14 dB, the simulations on an 80 m x 80 m factory shop floor show that low-bandwidth systems, such as Bluetooth and WirelessHART, can coexist with high-bandwidth and low-latency AR/VR applications running on Wi-Fi mobile terminals if the number of 11 Wi-Fi access points and 80 mobile AR/VR devices transmitting simultaneously is not exceeded.

Abstract
This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets-one placed over and one placed under a mattress; furthermore, a segmentation data layer was added where different body parts are identified using the pressure data from the sensors over the mattress. The data included were gathered from 60 healthy volunteers distributed among the different gathered characteristics: namely sex, weight, and height. This dataset can be used for position classification, assessing the viability of sensors placed under a mattress, and in applications regarding bedded or lying people or sleep related disorders. Dataset The dataset is available on GitHub: https://github.com/rdionisio1403/PoPu/. Dataset License The dataset is available under Creative Commons (CC0).