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

Face Mask Detector for Raspberry Pi Based on Computer Vision and Edge Computing

The pandemic of the coronavirus disease 2019 has shown weakness and threats in various fields of human activity. In turn, the World Health Organization has recommended different preventive measures to decrease the spreading of coronavirus. Nonetheless, the world community ought to be ready for worldwide pandemics in the closest future. One of the most productive approaches to prevent spreading the virus is still using a face mask. This case has required staff who would verify visitors in public areas to wear masks. The aim of this paper was to identify persons remotely who wore masks or not, and also inform the personnel about the status through the message queuing telemetry transport as soon as possible using the edge computing paradigm. To solve this problem, we proposed to use the Raspberry Pi with a camera as an edge device, as well as the TensorFlow framework for pre-processing data at the edge. The offered system is developed as a system that could be introduced into the entrance of public areas. Experimental results have shown that the proposed approach was able to optimize network traffic and detect persons without masks. This study can be applied to various closed and public areas for monitoring situations. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Detection of COVID-19 for image dataset by using Deep learning algorithm

This COVID-19 study uses a new way of looking at data to shed light on important topics and societal problems. After digesting specific interpretations, experts' points of view are looked at: We'll study and categorize these subfields based on their importance and influence in the academic world. Web-based education, cutting-edge technologies, AI, dashboards, social networking, network security, industry titans (including blockchain), safety, and inventions will be discussed. By combining chest X-ray images with machine learning, the article views provide element breadth, ideal understanding, critical issue detection, and hypothesis and practice concepts. We've used machine learning techniques in COVID-19 to help manage the pandemic flow and stop infections. Statistics show that the hybrid strategy is better than traditional ones. © 2022 IEEE.

LLDSNet: Limited labeled data segmentation network for COVID-19 CT lesion segmentation

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks. © 2023 SPIE.

ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost. © 2023 ACM.

5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.

The COVID-19 outbreak has stimulated the digital transformation of antiquated healthcare system to a smart hospital, enabling the personalised and remote healthcare services. To augment the functionalities of these intelligent healthcare systems, 5G & B5G heterogeneous network has emerged as a robust and reliable solution. But the pivotal challenge for 5G & B5G connectivity solutions is to ensure flexible and agile service orchestration with acknowledged Quality of Experience (QoE). However, the existing radio access technology (RAT) selection strategies are incapacitated in terms of QoE provisioning and Quality of Service (QoS) maintenance. Therefore, an intelligent QoE aware RAT selection architecture based on software-defined wireless networking (SDWN) and edge computing has been proposed for 5G-enabled healthcare network. The proposed model leverages the principles of invalid action masking and multi-agent reinforcement learning to allow faster convergence to QoE optimised RAT selection policy. The analytical evaluation validates that the proposed scheme outperforms the other existing schemes in terms of enhancing personalised user-experience with efficient resource utilisation.

A Novel Edge-Assisted IoT-ML-Based Smart Healthcare Framework for COVID-19

The lack of modern technology in healthcare has led to the death of thousands of lives worldwide due to COVID19 since its outbreak. The Internet of Things (IoT) along with other technologies like Machine Learning can revolutionize the traditional healthcare system. Instead of reactive healthcare systems, IoT technology combined with machine learning and edge computing can deliver proactive and preventive healthcare services. In this study, a novel healthcare edge-assisted framework has been proposed to detect and prognosticate the COVID-19 suspects in the initial phases to stop the transmission of coronavirus infection. The proposed framework is based on edge computing to provide personalized healthcare facilities with minimal latency, short response time, and optimal energy consumption. In this paper, the COVID-19 primary novel dataset has been used for experimental purposes employing various classification-based machine learning models. The proposed models were validated using k cross-validation to ensure the consistency of models. Based on the experimental results, our proposed models have recorded good accuracies with highest of 97.767% by Support Vector Machine. According to the findings of experiments, the proposed conceptual model will aid in the early detection and prediction of COVID-19 suspects, as well as continuous monitoring of the patient in order to provide emergency care in case of medical volatile situation.

Face Mask Classification based on Transfer Learning and MobileNet V2

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts: with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19. © 2022 ACM.

A Wireless Virtual Reality-Based Multimedia-Assisted Teaching System Framework under Mobile Edge Computing

In recent years, virtual reality (VR) has gradually entered the daily education and teaching activities from pure scientific research. In the area of assistance teaching, some typical computer softwares still play some important roles. This makes remote teaching activities can just learn voice, yet cannot possess the feeling of realistic existence. Especially in scenario of COVID-19, remote teaching activities with proper perceptibility are in urgent demand. To deal with the current challenge, this paper proposes a wireless VR-based multimedia-assisted teaching system framework under mobile edge computing networks. In this framework, cooperative edge caching and adaptive streaming based on viewport prediction are adopted to jointly improve the quality of experience (QoE) of VR users. First, we investigated the resource management problem of caching and adaptive streaming in this framework. Considering the complexity of the formulated problem, a distributed learning scheme is proposed to solve the problem. The experimental data are verified and the experimental results prove that the studied methods improve the performance of user QoE. [ FROM AUTHOR] Copyright of Journal of Circuits, Systems & Computers is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Private Blockchain-Based AI-Envisioned Home Monitoring Framework in IoMT-Enabled COVID-19 Environment

Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.

A review of edge computing in healthcare Internet of things: Theories, practices and challenges

The pandemic has forced industries to move immediately their critical workload to the cloud in order to ensure continuous functioning. As cloud computing expansions pace and organisations strive for methods to increase their network, agility and storage, edge computing has shown to be the best alternative. The healthcare business has a long history of collaborating with cutting-edge information technology, and the Internet of Things (IoT) is no exception. Researchers are still looking for substantial methods to collect, view, process, and analyze data that can signify a quantitative revolution in healthcare as devices become more convenient and smaller data become larger. To provide real-time analytics, healthcare organisations frequently deploy cloud technology as the storage layer between system and insight. Edge computing, also known as fog computing, allows computers to perform important analyses without having to go through the time-consuming cloud storage process [15, 16]. For this form of processing, speed is key, and it may be crucial in constructing a healthcare IoT that is useful for patient interaction, inpatient treatment, population health management and remote monitoring. We present a thorough overview to highlight the most recent trends in fog computing activities related to the IoT in healthcare. Other perspectives on the edge computing domain are also offered, such as styles of application support, techniques and resources [17]. Finally, necessity of edge computing in era of Covid-19 pandemic is addressed. © The Institution of Engineering and Technology 2022.

Beyond the Exscalate4CoV Project: LIGATE and REMEDI4ALL Projects

In the last 2 years, the SARS-CoV-2 (COVID-19) pandemic demonstrated that rapid response to outbreaks with readily effective treatments represents a primary health and societal priority. At the same time, we became conscious that technological resources are often not used in the most efficient manner. The LIGATE and REpurposing MEDIcines For All (REMEDI4ALL) projects started on the large-scale mobilization efforts of the EXaSCale smArt pLatform Against paThogEns (Exscalate4Cov) project with the aim to apply cutting-edge technologies in drug discovery, sustain the fight against future pandemics, and promote the everyday fight against rare diseases. In particular, the LIGATE project, using the drug-discovery platform Exscalate, intends to boost the virtual screening of drug campaigns at an extreme scale in terms of performance and streamline the drug-development process. The aim of the REMEDI4ALL project is to collect sciQ1entific expertise and innovative technology platforms for the repurposing of medicines to treat rare diseases or other pathologic conditions with no current therapy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Classification from X-Ray Images: An Approach to Implement Federated Learning on Decentralized Dataset

The COVID-19 pandemic has devastated our daily lives, leaving horrific repercussions in its aftermath. Due to its rapid spread, it was quite difficult for medical personnel to diagnose it in such a big quantity. Patients who test positive for Covid-19 are diagnosed via a nasal PCR test. In comparison, polymerase chain reaction (PCR) findings take a few hours to a few days. The PCR test is expensive, although the government may bear expenses in certain places. Furthermore, subsets of the population resist invasive testing like swabs. Therefore, chest X-rays or Computerized Vomography (CT) scans are preferred in most cases, and more importantly, they are non-invasive, inexpensive, and provide a faster response time. Recent advances in Artificial Intelligence (AI), in combination with state-of-the-art methods, have allowed for the diagnosis of COVID-19 using chest x-rays. This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme. In order to build a progressive global COVID-19 classification model, two edge devices are employed to train the model on their respective localized dataset, and a 3-layered custom Convolutional Neural Network (CNN) model is used in the process of training the model, which can be deployed from the server. These two edge devices then communicate their learned parameter and weight to the server, where it aggregates and updates the global model. The proposed model is trained using an image dataset that can be found on Kaggle. There are more than 13,000 X-ray images in Kaggle Database collection, from that collection 9000 images of Normal and COVID-19 positive images are used. Each edge node possesses a different number of images;edge node 1 has 3200 images, while edge node 2 has 5800. There is no association between the datasets of the various nodes that are included in the network. By doing it in this manner, each of the nodes will have access to a separate image collection that has no correlation with each other. The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset, and the findings that we have obtained are quite encouraging. © 2023 Tech Science Press. All rights reserved.

A game theory-based COVID-19 close contact detecting method with edge computing collaboration.

People all throughout the world have suffered from the COVID-19 pandemic. People can be infected after brief contact, so how to assess the risk of infection for everyone effectively is a tricky challenge. In view of this challenge, the combination of wireless networks with edge computing provides new possibilities for solving the COVID-19 prevention problem. With this observation, this paper proposed a game theory-based COVID-19 close contact detecting method with edge computing collaboration, named GCDM. The GCDM method is an efficient method for detecting COVID-19 close contact infection with users' location information. With the help of edge computing's feature, the GCDM can deal with the detecting requirements of computing and storage and relieve the user privacy problem. Technically, as the game reaches equilibrium, the GCDM method can maximize close contact detection completion rate while minimizing the latency and cost of the evaluation process in a decentralized manner. The GCDM is described in detail and the performance of GCDM is analyzed theoretically. Extensive experiments were conducted and experimental results demonstrate the superior performance of GCDM over other three representative methods through comprehensive analysis.

Leading Edge Use of Technology for Teacher Professional Development in Indian Schools

This paper presents a case study of a decade-long technology-enabled teacher professional development (TPD) initiative for government-run school teachers in India. The TPD aimed at capacitating teachers in integrating project-based or constructivist learning with technology in curriculum and pedagogy. Teachers are central to the teaching–learning processes, and hence capacitating them to leverage digital technologies confidently is essential to improve the quality of education imparted to learners. This paper focuses on the use of innovative technologies leading to the education of teachers. A decade of TPD is divided into three phases providing an analytical framework for the evolving technologies and pedagogies across the phases. Documents, resources and tools used for the TPD activities, researchers' first-hand experiences and documented research studies supported the comparative analysis of the three phases of TPD. This comparison highlighted leading-edge technologies influencing changes in TPD delivery mode, learning support, pedagogy, scale, etc., across the phases. The paper also maps the interrelationship between technologies and pedagogies of TPD, suggesting various features of innovations such as continuous, practice-based, collaborative, scalable, shareable, transferable, and adaptable.

Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules

The major challenges encountered by medical researchers in developing new drugs are time consumption, increased cost, establishing a safety profile for the drugs, poor solubility, and inadequate experimental data. In its theoretical aspects, chemical graph theory plays a vital role in drug design and development by analyzing the structural parameters of molecules. Topological indices aim at the mathematical representation of a molecular structure, which is used to analyze the effectiveness of drugs and enhance the drug development process. In this study, we consider certain recently used drugs such as dexamethasone, molnupiravir, nirmatrelvir, ivermectin, ribavirin, baricitinib, favipiravir, duvelisib, L‐ascorbic acid, sofosbuvir, remdesivir, and pioglitazone for omicron, delta and other variants of coronaviruses. For these drug molecules, we propose a generalized form of reverse degree parameters and compute their associated topological indices with limiting behaviors. We undertake QSPR study on the potential of generalized reverse‐degree indices using linear and cubic regression models. [ FROM AUTHOR] Copyright of International Journal of Quantum Chemistry is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Auto-detection of social contact for fighting COVID-19 pandemic.

Because of the high infectivity of COVID-19, it is essential to detect the close contacts of patients as soon as possible to contain the outbreak of the epidemic. However, due to the level of technological development, the current methods and research on contact detection require manual participation. This paper proposes a future oriented automation method, which uses mobile agents loaded on sensing devices and edge coordinators to form a multi-agent system on the street. Based on perception, tracking and edge-computing, the contact probability between infected people and pedestrians is estimated. A series of simulations provide the comparison of parameters in application deployment. The simulation results show that the proposed street expropriation mode and edge-computing algorithm can further improve the detection rate. (English) [ FROM AUTHOR] 由于新冠病毒的高传染性，及早发现患者的密切接触者对于遏制疫情爆发至关重要。而受限于技术发 展的水平，目前关于接触检测的方法和研究均需人工参与。提出了一种面向未来的自动化方法，利用加载在感 知设备上的移动智能体和边缘协调器在街道上组成多智能体系统，基于对感染者的感知、跟踪和边缘计算，实现 了感染者与行人之间的接触概率估算。系列仿真给出了应用部署中的参数比较。仿真结果表明，提出的街道征 用模式及边缘计算算法可以进一步改善检测率。 (Chinese) [ FROM AUTHOR] Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Device-Edge-Cloud Collaborative Acceleration Method Towards Occluded Face Recognition in High-Traffic Areas

Wearing masks can effectively inhibit the spread and damage of COVID-19. A device-edge-cloud collaborative recognition architecture is designed in this paper, and our proposed device-edge-cloud collaborative recognition acceleration method can make full use of the geographically widespread computing resources of devices, edge servers, and cloud clusters. First, we establish a hierarchical collaborative occluded face recognition model, including a lightweight occluded face detection module and a feature-enhanced elastic margin face recognition module, to achieve the accurate localization and precise recognition of occluded faces. Second, considering the responsiveness of occluded face detection services, a context-aware acceleration method is devised for collaborative occluded face recognition to minimize the service delay. Experimental results show that compared with state-of-the-art recognition models, the proposed acceleration method leveraging device-edge-cloud collaborations can effectively reduce the recognition delay by 16%while retaining the equivalent recognition accuracy. IEEE

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

Non-Cooperative Game for Edge Server Placement in Wireless Body Area Network

The outbreak of the coronavirus disease 2019 (COVID-19) has become the worst public health event in the whole world, threatening the physical and mental health of hundreds of millions of people. However, because of the high survivability of the virus, it is impossible for humans to eliminate viruses completely. For this reason, it is particularly important to strengthen the prevention of the transmission of viruses and monitor the physical status of the crowd. Wireless sensors are a key player in the fight against the current global outbreak of the Covid-19 pandemic, where they are playing an important role in monitoring human health. The Wireless Body Area Network (WBAN) composed of these wireless sensor devices can monitor human health data without interference for a long time, and update the data in almost real time through the Internet of Things (IoT). However, because the data monitored by the devices is relatively large and the transmission distance is long, only transmitting the data to medical centers through the personal devices (PB) cannot get feedback in time. We propose a non-cooperative game-based server placement method, which is named ESP-19 to improve the efficiency of transmission data of wireless sensors. In this paper, experimental tests are conducted based on the distribution of Shanghai Telecom's base stations, and then the performance of ESP-19 is evaluated. The results show that the proposed method in this paper outperforms the comparison method in terms of service delay. © 2022 IEEE.