ABSTRACT
This work aims to address and emphasize the importance of the fifth-generation mobile networks (5G) rollout in the smart cities, focusing on the South American countries and bringing a theoretical analysis about the implementation in each country from the perspective of the national regulatory agencies, as well as the significance of this system for the future of the countries with the benefits expected for the 5G networks and their application in the smart cities. It was made a bibliographic review of the involved technical concepts and a collection of information from the regulatory agencies and telecommunication companies of all the countries and one French territory of South America, aiming to identify in which stage of roadmap implementation each country is, considering that many countries in other continents (e.g., South Korea and the USA) already have an implemented and functional 5G architecture. The whole region suffered a delay due to COVID-19, but all the studied countries already have or are creating the required standards for an implementation that follows the international guidelines, which shows that, though they are not as evolved as other countries, their rulers and administrators are concerned about this global trend. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
One of the greatest challenges for a traffic control system is to synchronize the flow of vehicles to prevent traffic jams. This issue gets worse when there are priority vehicles, such as ambulances, trying to move through the traffic. Given the current situation, with the COVID-19 pandemic, and the trends of smart cities, in this work, we propose and simulate a traffic control system that prioritizes ambulances within large urban centers, using Fuzzy logic and IoT devices. The simulation of our proposed model was performed on the software Dojot, which is an open platform for IoT modeling. It addressed a real situation, in a path that is usually used by ambulances to get to a reference hospital in the city of Campinas, Brazil. The proposed traffic control system can also be used after the COVID-19 pandemic is over in order to improve traffic flow for other priority vehicles (e.g., firefighters and police) and increase people’s life quality within smart cities. © 2022, Springer Nature Switzerland AG.
ABSTRACT
The 2019 coronavirus disease (COVID-19) pandemic has contaminated millions of people, resulting in high fatality rates. Recently emerging artificial intelligence technologies like the convolutional neural network (CNN) are strengthening the power of imaging tools and can help medical specialists. CNN combined with other sensors creates a new solution to fight COVID-19 transmission. This paper presents a novel method to detect coughs (an important symptom of COVID-19) using a K-band continuous-wave Doppler radar with most popular CNNs architectures: AlexNet, VGG-19, and GoogLeNet. The proposed method has cough detection test accuracy of 88.0% using AlexNet CNN with people 1 m away from the microwave radar sensor, test accuracy of 80.0% with people 3 m away from the radar sensor, and test accuracy of 86.5% with a single mixed dataset with people 1 m and 3 m away from the radar sensor. The K-band radar sensor is inexpensive, completely camera-free and collects no personally-identifying information, allaying privacy concerns while still providing in-depth public health data on individual, local, and national levels. Additionally, the measurements are conducted without human contact, making the process proposed in this work safe for the investigation of contagious diseases such as COVID-19. The proposed cough detection system using microwave radar sensor has environmental robustness and dark/light-independence, unlike traditional cameras. The proposed microwave radar sensor can be used alone or in group with other sensors in a fusion sensor system to create a robust system to detect cough and other movements, mainly if using CNNs.
ABSTRACT
The increase in the number world population of elderly citizens, as well as those who live in solitude, needs an immediate solution with an intelligent monitoring system at home. In this work, we present an intelligent fall-detection system based on IoT to monitor the elderly with your privacy-protected. Currently, fall detection has attracted significant research attention and deep learning has shown promising performance in this task using conventional cameras. However, these traditional methods pose a risk of the leakage of personal privacy. This work proposes a novel fall-detection system that uses a continuous-wave Doppler radar sensor to acquisition the elderly movements and sends this information thought the internet to a server with deep learning using a convolutional neural network (CNN) that identifies the fall. The radar sensor is inexpensive, completely camera-free, and collects no personally identifiable information, thereby allaying privacy concerns. Additionally, unlike traditional cameras, it has environmental robustness and dark/light-independence. The proposed system obtained 99.9% accuracy in detecting falls by using the GoogleNet convolutional neural network. The proposed system is also capable of detecting other types of movements in addition to those tested, including the detection of diseases such as COVID-19 through the cough movement. © 2020 IEEE.