Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Design and development of a mobile application for level monitoring of oxygen cylinders using block chain technology

Due to improvements in information and communication technology and growth of sensor technologies, Internet of Things is now widely used in medical field for optimal resource management and ubiquitous sensing. In hospitals, many IoT devices are linked together via gateways. Importance of gateways in modernization of hospitals cannot be overstated, but their centralized nature exposes them to a variety of security threats, including integrity, certification, and availability. Block chain technology for level monitoring in oxygen cylinders is a scattered record containing the data related to oxygen levels in the cylinder, patient's name, patient's ID number, patient's medical history, and all connected information carried out and distributed among the hospitals (nodes) present in the locality (network). Designing an oxygen level monitoring technique in an oxygen cylinder used as the support system for COVID-19-affected patients is a challenging task. Monitoring the level of oxygen in the cylinders is very important because they are used for saving the lives of the patients suffering from COVID-19. Not only the COVID-19 patients are dependent on this system, but this system will also be helpful for other patients who require oxygen support. The present scenario many COVID-19 hospitalized patients rely upon oxygen supply through oxygen cylinders and manual monitoring of oxygen levels in these cylinders has become a challenging task for the healthcare professionals due to overcrowding. If this level monitoring of oxygen cylinders are automated and developed as a mobile App, it would be of great use to the medical field, saving the lives of the patients who are left unmonitored during this pandemic. This proposal is entitled to develop a system to measure oxygen level using a smartphone App which will send instantaneous values about the level of the oxygen inside the cylinder. Pressure sensors and load cell are fitted to the oxygen cylinders, which will measure the oxygen content inside the cylinder in terms of the pressure and weight. The pressure sensors and load cells are connected to the Arduino board and are programmed to display the actual level of oxygen inside the cylinder in terms of numerical values. A beep sound is generated as an indicator to caution the nurses and attendants of the patients regarding the level of the oxygen inside the cylinder when it is only 15% of the total oxygen level in the cylinder in correlation to the pressure and weight. The signal with respect to the level corresponding to the measured pressure and weight of the cylinder is further transmitted to the monitoring station through Global System for Mobile communication (GSM). Graphical display is used at monitoring end to indicate the level of oxygen inside all oxygen cylinders to facilitate actions like 100% full, 80% full, 60% full, 40% full, 20% full which states that either the oxygen cylinder is in good condition, or requires a replacement of empty cylinders with filled ones in correlation to the pressure and weight being sensed by the sensors. The levels of the oxygen monitored inside the cylinder and other related data can also be stored on a cloud storage which will facilitate the retrieval of the status at any point of time, as when required by the physicians and nurses. These results reported, are valued in monitoring the level of the oxygen cylinder remotely connected to the patients, affected by COVID-19, using a smartphone App. This mobile phone App is an effective tool for investigating the oxygen cylinder level used as a life-support system for COVID-19-affected patients. A virtual model of the partial system is developed using TINKER CAD simulation package. In real time, the sensor data analysis with cloud computing will be deployed to detect and track the level of the oxygen cylinders. © 2023 Elsevier Inc. All rights reserved.

Quantifying the impact of meteorological factors and green infrastructure location on particulate matter (PM) mitigation in North Macedonia using sensor collected data

Low quality of the air is becoming a major concern in urban areas. High values of particulate matter (PM) concentrations and various pollutants may be very dangerous for human health and the global environment. The challenge to overcome the problem with the air quality includes efforts to improve healthy air not only by reducing emissions, but also by modifying the urban morphology to reduce the exposure of the population to air pollution. The aim of this contribution is to analyse the influence of the green zones on air quality mitigation through sensor measurements, and to identify the correlation with the meteorological factors. Actually, the objective focuses on identifying the most significant correlation between PM2.5 and PM10 concentrations and the wind speed, as well as a negative correlation between the PM concentrations and wind speed across different measurement locations. Additionally, the estimation of slight correlation between the PM concentrations and the real feel temperature is detected, while insignificant correlations are found between the PM concentrations and the actual temperature, pressure, and humidity. In this paper the effect of the pandemic restriction rules COVID-19 lockdowns and the period without restriction are investigated. The sensor data collected before the pandemic (summer months in 2018), during the global pandemic (summer months 2020), and after the period with restriction measures (2022) are analysed.

Optimizing Presence Sensing Lighting for Energy Efficiency and User Behavioral Needs in Small Swedish Homes

A hybrid workstyle is becoming more common post-COVID-19, and longer occupancy hours at home are increasing household electricity consumption. Small homes are regarded as a potential for improving energy efficiency in the residential sector, and a home consists of mixed-function spaces with dynamic occupancy behaviors. These underpin the opportunity to optimize presence sensing lighting in small homes for energy efficiency and user-behavioral needs. A comprehensive overview of presence sensing approaches, comparing four types of non-wearable sensors connected to home lighting is made. A bibliometric mapping of the reviewed literature visually reinforces a significant research gap that presence sensing studies were not connected to home lighting but inclined toward the commercial and institutional context. Next, a non-exhaustive example of commercially available presence sensing products applicable to residential lighting for small homes is analyzed, and their general characteristics and technologies are synthesized. The literature and product overview identified five significant product knowledge gaps. Incorporating the gathered information leads to the proposal of a conceptual flexible radar-based sensor (prototype design), addressing a wish list with three important criteria to optimize future presence sensing lighting in a mixed-function small home. Future radar sensing studies are expected to develop an anticipatory lighting system that processes real-time multi-user vital signals for smarter localized and personalized lighting options for (small) living environments.

Nanosponges: As a Dynamic Drug Delivery Approach for Targeted Delivery

Recent advancements in nanotechnology have resulted in improved medicine delivery to the target site. Nanosponges are three-dimensional drug delivery systems that are nanoscale in size and created by cross-linking polymers. The introduction of Nanosponges has been a significant step toward overcoming issues such as drug toxicity, low bioavailability, and predictable medication release. Using a new way of nanotechnology, nanosponges, which are porous with small sponges (below one microm) flowing throughout the body, have demonstrated excellent results in delivering drugs. As a result, they reach the target place, attach to the skin's surface, and slowly release the medicine. Nanosponges can be used to encapsulate a wide range of medicines, including both hydrophilic and lipophilic pharmaceuticals. The medication delivery method using nanosponges is one of the most promising fields in pharmacy. It can be used as a biocatalyst carrier for vaccines, antibodies, enzymes, and proteins to be released. The existing study enlightens on the preparation method, evaluation, and prospective application in a medication delivery system and also focuses on patents filed in the field of nanosponges.Copyright © 2023 The Authors.

Design for an Intelligent Waste Classifying System: A Case Study of Plastic Bottles

The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles' surfaces. The proposed prototype's reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants. © 2013 IEEE.

A survey of automatic contact tracing approaches using bluetooth low energy

To combat the ongoing Covid-19 pandemic, many new ways have been proposed on how to automate the process of finding infected people, also called contact tracing. A special focus was put on preserving the privacy of users. Bluetooth Low Energy as base technology has the most promising properties, so this survey focuses on automated contact tracing techniques using Bluetooth Low Energy. We define multiple classes of methods and identify two major groups: systems that rely on a server for finding new infections and systems that distribute this process. Existing approaches are systematically classified regarding security and privacy criteria.Copyright © 2021 ACM.

Portable blood glucose meter system for self-monitoring of diabetic patients

The appearance of COVID-19 changed the lifestyle of many people as it spread rapidly around the world, causing concern to the entire health system due to the high number of infected and leading to a general confinement, changing the lifestyle and eating habits of many people causing diabetes, which is a disease caused by the high level of glucose in the blood, which can generate serious problems in the health of the person since it has no cure, this progressive disease is controlled or monitored by conventional glucometer equipment that generates pain in patients because they require blood samples to measure glucose, worse for those diabetics who must have the measurement several times a day. In view of this problem, this article will make a portable blood glucose meter system for the self-monitoring of diabetic patients and determine the blood sugar level to visualize it by means of a screen, with this system the measurement will be made without pain and will show the value of the glucose level accurately, Helping diabetic patients who perform monitoring several times a day. Through the development of l system, it was observed that it works in the best way with an efficiency of 96.97% in the measurement of glucose, when comparing with others equipment glucometers obtained a relative error of 2.99%, being an error accepted to approach the real value. © 2023 IEEE.

Infrared Temperature Detection Using MLX90614 Sensor for Wearable Applications

Body temperature is a significant vital sign that can provide great insight as to the state of health of a person. Nowadays, body temperatures are monitored as often as a precaution for the COVID-19 virus. This can be achieved with the use of wearables, which can be non-invasive and convenient for anybody to use. This study aims to design and construct a wearable that can accurately detect the body temperature of a person using the MLX90614 sensor as well as an I2C enabled LCD to allow the user to monitor their temperature at a moment's notice. © 2023 IEEE.

A smart healthcare monitoring system for patients using IoT and cloud computing

Health monitoring systems are rapidly growing in recent times, Continuous monitoring of the patients is one of the big challenges for hospitals. Smart systems have been established to track the patient present health status;we focus on monitoring the patient's blood pressure, body temperature, Heart rate. In this project we use Arduino Mega 2560 which is a microcontroller board based on the ATmega2560 comments. In this paper, Embedded C language by using Arduino is used to obtain the sensor values. IOT data cloud is used in this project. IOT is used in healthcare system to track the patients' health condition as a monitoring device. Cloud computing develops as a platform for IoT data storage, processing and analytics because of its simplicity, expandability and affordability. Transmit sensor values to Arduino and it sends to GSM and WIFI module to monitor the parameters of the patients. In this project the notifications of patient's health status are sent to the caretaker and nurse, simultaneously it is updated in webpages also for the doctor's reference. Taking in account, COVID 19 Pandemic is highly infectious and spreadable disease, so to maintain the social distance, this monitoring system is needed. It reduces the need for face-to-face appointments with doctors in hospital. ECG sensor is also used to decide the heart activity of the patients. This project aims for the patients who are in continuous monitoring and bedridden. The GSM and IOT technologies give the architecture for healthcare in this project. © 2023 Author(s).

Face Mask And Body Temperature Detection In Sensor Enabled Automated Door Control System

Developing an automatic door-opening system that can recognize masks and gauge body temperature is the aim of this project. The new Corona Virus (COVID-19) is an unimaginable pandemic that presents the medical industry with a serious worldwide issue in the twenty-first century. How individuals conduct their lives has substantially changed as a result. Individuals are reluctant to seek out even the most basic healthcare services because of the rising number of sick people who pass away, instilling an unshakable terror in their thoughts.This paper is about the Automatic Health Machine (AHM). In this dire situation, the government provided the people with a lot of directions and information. Apart from the government, everyone is accountable for his or her own health. The most common symptom of corona infection is an uncontrollable rise in body temperature. In this project, we create a novel device to monitor people's body temperatures using components such as an IR sensor and temperature sensor. © 2023 IEEE.

Observational study of peripheral skin temperature changes following spinal anaesthesia for caesarean delivery

Introduction: Effective spinal anaesthesia for caesarean delivery (CD) is assumed to cause bilateral sympathetic blockade with increased feet skin temperature due to vasodilatation [1]. There has been no published study of peripheral skin temperature measurements during spinal anaesthesia for CD. Our study investigated foot skin temperature changes as spinal anaesthesia was established. Method(s): A single centre, prospective observational study with ethics committee approval (IRAS No. 263967). With informed consent, 60 healthy parturients, 37-42 weeks' gestation with singleton pregnancy scheduled for category 4 CD with spinal anaesthesia were recruited. Standard spinal anaesthesia used 0.5% hyperbaric bupivacaine and diamorphine with IV Phenylephrine and fluids. Skin temperature was measured on the dorsum of both feet with Covidien Mon-a-Therm© skin thermistor sensors prior to intrathecal injection and every minute after until completion of surgery. Theatre room temperature and ambient temperature under surgical drapes were recorded. Two controls were recruited. Result(s): All participants had successful spinal anaesthesia. The Figure shows mean (95% CI) skin temperature changes of both feet of participants during spinal anaesthesia and for controls. The maximum rate of skin temperature increase occurred 5-12 minutes after spinal injection with temperature change plateauing after 30 mins. The mean temperature range was 5.54degreeC (min = 29.7degreeC;max = 35.2degreeC). Discussion(s): This study characterises for the first time the peripheral temperature changes in the feet that occur with sympathetic block after spinal anaesthesia in parturients. Increased bilateral foot skin temperatures occur within 10 minutes of spinal injection. This may be useful for determining successful spinal anaesthesia for CD in addition to other assessments [2]. The insights may be useful for assessing epidural analgesia. The study was supported by an OAA research grant. Data collected by ROAR group.Copyright © 2023 Elsevier Ltd

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

A comprehensive study on respiratory parameters using bluetooth enabled multilayered COVID mask: A preventive measure

Within the contemporary state of affairs we all realize the significance of wearing a masks, though we are wear a mask, in some situations we're affected to Covid. In that case our mask is designed in a way which is multilayered and reusable. The primary gain of our masks is that we're setting a breathing sensor (humidity sensor) within the masks so that any sort of respiratory trouble may be detected in a much simpler way with the use of blue tooth. In order too that respiration troubles of human beings may be quarantined and spreading may be stopped at the primary level itself. The ongoing 2nd wave of Covid 19 pandemics has ended in an global scarcity of face mask and the uncooked chemical compounds that move into them, prompting humans to make their personal mask from of regular items. N95/p2 respiration masks are one of the vital components for decreasing the spread of the Covid 19 virus and shielding frontline humans. With the wearable multi layer air cleanser masks our idea is to replace it with homemade masks worn through many and also which are disposable and reusable ones. The layout also makes it viable to wear the unit with no trouble for hours on give up. This might be made light-weight so that users can utilise it with ease. Further survey has been made among a group of people by wearing our mask and their humidity level has been noted thorough the software developed. © 2023 Author(s).

COVID bell - A smart doorbell solution for prevention of COVID-19

The article introduces a novel strategy for efficiently mitigating COVID-19 distribution at the local level due to contact with any surfaces. Our project aims to be a critical safety shield for the general people in the fight against the epidemic. An ultrasonic sensor is integrated with the automated doorbell system to ring the doorbell with a hand motion. A temperature sensor Mlx90614 is also included in the system, which records the temperature of the person standing in front of the door. The device also includes a camera module that captures the image of the person standing at the front entrance. The captured image is processed through an ML model which runs at over 30 fps to detect whether or not the person is wearing a mask. The image and the temperature of the person standing outside are sent to the owner through the configured iOS application. If the person outside is wearing a mask, one can open the door through the app itself and permit the entry of the person standing outside thereby integrating the edge device with an app for a better user experience. The system helps in reducing physical contact, and the results obtained are at par with the already existing solutions and provide a few advantages over them. © 2023 the author(s), published by De Gruyter.

Vital sign deviations in hospitalized patients with COVID-19: An observational study with continuous monitoring

Background: The purpose was to determine the frequency and duration of vital sign deviations in acutely admitted patients in general wards with confirmed COVID-19 infection using continuous wireless vital sign monitoring. Material(s) and Method(s): Patients were equipped with two wireless sensors live-transmitting respiratory rate (RR), heart rate (HR) and peripheral oxygen saturation (SpO2). Frequency and duration of vital sign deviations were compared with manual point measurements performed by clinical staff according to the national Early Warning Score (EWS), assuming linear relationship between EWS point measurements. Result(s): Continuous monitoring detected episodes of SpO2 < 92% for more than 60 consecutive min in 92% of patients versus 42% of patients detected by EWS (p < 0.0001). Events of desaturation with SpO2 < 88% for more than 10 min was registered in 87% with continuous monitoring versus 27% with EWS (p < 0.0001). Desaturation with SpO2 < 80% for more than 1 min was detected in 76% with continuous monitoring versus 6% with EWS (p < 0.0001). 70% of patients had episodes of tachypnea with RR >24 breaths per minute >5 min detected with continuous monitoring versus 36% assessed by EWS (p = 0.0001). Episodes of HR >111 for >60 min was recorded in 51% versus 24% (p = 0.0002). Conclusion(s): Moderate and severe episodes of desaturation, tachypnea, and tachycardia during hospital admission in patients with COVID-19 infection are common and most often not detected by routine manual measurements.

IoT enabled Covid Standard Operating Procedure system

COVID-19, is caused by the transmission of SARS-CoV-2 through direct or indirect contact with infected people though respiratory droplets has transitioned from a pandemic to an endemic but is still regarded as active by WHO. Restrictions and lockdowns were lifted as the situation became endemic, but the previous measures had to be kept in place. By developing a module that includes temperature monitoring, face mask detection, a non-contact sanitizer dispenser, and door automation that operates based on the number of individuals inside a closed area in order to maintain social distance, our project aims to incorporate these precautions into our everyday language. As a part of making the new normal easily adaptable, we also introduce a webpagebased reservation system, which wm essentially display the current count and also help in reducing the waiting periods. © 2023 IEEE.

Reduce the spread risk of covid-19 based on clinical fusion data and monitoring system in wireless sensor network

The expression "COVID-19” has been the fiercest but most trending Google search since it first appeared in November 2019. Due to advances in mobile technology and sensors, Healthcare systems based on the Internet of Things are conceivable. Instead of the traditional reactive healthcare systems, these new healthcare systems can be proactive and preventive. This paper suggested a framework for real-time suspect detection based on the Internet of Things. In the early phases of predicting COVID-19, the framework evaluates the existence of the virus by extracting health variables obtained in real-time from sensors and other IoT devices, in order to better understand the behavior of the virus by collecting symptom data of COVID-19, In this paper, four machine learning models (Random Forest, Decision Tree, K-Nearest Neural Network, and Artificial Neural Network) are proposed, these data and applied as a machine learning model to obtain high diagnostic accuracy, however, it is noted that there is a problem when collecting clinical fusion data that is scarce and unbalanced, so a dataset augmented by Generative Adversarial Network (GAN) was used. Several algorithms achieved high levels of accuracy (ACC), including Random Forest (99%), and Decision Tree (99%), K-Nearest Neighbour (98%), and Artificial Neural Network (99%). These results show the ability of GANs to generate data and their ability to provide relevant data to efficiently manage Covid-19 and reduce the risk of its spread through accurate diagnosis of patients and informing health authorities of suspected cases. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

Cough Detection for Prevention Against the COVID-19 Pandemic

This paper proposes a novel and robust technique for remote cough recognition for COVID-19 detection. This technique is based on sound and image analysis. The objective is to create a real-time system combining artificial intelligence (AI) algorithms, embedded systems, and network of sensors to detect COVID-19-specific cough and identify the person who coughed. Remote acquisition and analysis of sounds and images allow the system to perform both detection and classification of the detected cough using AI algorithms and image processing to identify the coughing person. This will give the ability to distinguish between a normal person and a person carrying the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Wearable Sensors in Daily Life: A Review

Wearable sensor technologies have improved people's daily lives through their applications in almost every field. Sensor technologies of inventive kinds are used in an extensive variety of applications in lifestyle, healthcare, fitness, manufacturing, etc. There have also been crucial issues in making significant improvements to the actual mechanical, electrical, and optical sensing methods mainly in upgrading the precision of identification of wearable sensors to various stimuli. With an extensive study of the basic demands in wearable device technology as of now, the road map becomes clearer for creating greater innovations in the future. This is a review that gives an outline of types of wearable sensors by the score that is utilized in daily life. © 2023 IEEE.