Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease.

Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.

CRISIS: Cyber-Physical Social Distancing Based on Multi-Modal Data From Mobile Devices

Multi-modal sensors on mobile devices (e.g., smart watches and smartphones) have been widely used to ubiquitously perceive human mobility and body motions for understanding social interactions between people. This work investigates the correlations between the multi-modal data observed by mobile devices and social closeness among people along their trajectories. To close the gap between cyber-world data distances and physical-world social closeness, this work quantifies the cyber distances between multi-modal data. The human mobility traces and body motions are modeled as cyber signatures based on ambient Wi-Fi access points and accelerometer data observed by mobile devices that explicitly indicate the mobility similarity and movement similarity between people. To verify the merits of modeled cyber distances, we design the localization-free CybeR-physIcal Social dIStancing (CRISIS) system that detects if two persons are physically non-separate (i.e., not social distancing) due to close social interactions (e.g., taking similar mobility traces simultaneously or having a handshake with physical contact). Extensive experiments are conducted in two small-scale environments and a large-scale environment with different densities of Wi-Fi networks and diverse mobility and movement scenarios. The experimental results indicate that our approach is not affected by uncertain environmental conditions and human mobility with an overall detection accuracy of 98.41% in complex mobility scenarios. Furthermore, extensive statistical analysis based on 2-dimensional (2D) and 3-dimensional (3D) mobility datasets indicates that the proposed cyber distances are robust and well-synchronized with physical proximity levels. © 2002-2012 IEEE.

Cyber-physical customer management for internet of robotic things-enabled banking

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymise the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations. Author

A New UAV-Based Social Distance Detector for COVID-19 Outbreaks Reduction, Using IoT, Computer Vision and Deep Learning Technologies

Nowadays, we are living in a dangerous environment and our health system is under the threatened causes of Covid19 and other diseases. The people who are close together are more threatened by different viruses, especially Covid19. In addition, limiting the physical distance between people helps minimize the risk of the virus spreading. For this reason, we created a smart system to detect violated social distance in public areas as markets and streets. In the proposed system, the algorithm for people detection uses a pre-existing deep learning model and computer vision techniques to determine the distances between humans. The detection model uses bounding box information to identify persons. The identified bounding box centroid's pairwise distances of people are calculated using the Euclidean distance. Also, we used jetson nano platform to implement a low-cost embedded system and IoT techniques to send the images and notifications to the nearest police station to apply forfeit when it detects people's congestion in a specific area. Lastly, the suggested system has the capability to assist decrease the intensity of the spread of COVID-19 and other diseases by identifying violated social distance measures and notifying the owner of the system. Using the transformation matrix and accurate pedestrian detection, the process of detecting social distances between individuals may be achieved great confidence. Experiments show that CNN-based object detectors with our suggested social distancing algorithm provide reasonable accuracy for monitoring social distancing in public places, as well. © 2022 Lavoisier. All rights reserved.

COVID-19 clinical medical relationship extraction based on MPNet

With the rapid development of biomedical research and information technology, the number of clinical medical literature has increased exponentially. At present, COVID-19 clinical text research has some problems, such as lack of corpus and poor annotation quality. In clinical medical literature, there are many medical related semantic relationships between entities. After the task of entity recognition, how to further extract the relationships between entities efficiently and accurately becomes very critical. In this study, a COVID-19 clinical trial data relationship extraction model based on deep learning method is proposed. The model adopts MPNet model, bidirectional-GRU (BiGRU) network, MAtt mechanism and Conditional Random Field inference layer integration architecture and improves the problem that static word vector cannot represent ambiguity through pre-trained language model. BiGRU network is used to replace the current Bi directional long short term memory structure and simplify the network structure of Long Short Term Memory to improve the training efficiency of the model. Through comparative experiments, the proposed method performs well in the COVID-19 clinical text entity relation extraction task. © 2023 The Authors. IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Secure Data Sharing in Medical Cyber-Physical system—a Review

With the increase in advanced technologies, the health industry has developed a lot recently. Medical cyber-physical system (MCPS) plays a vital role in this. It consists of various medical devices which are networked together for smooth and efficient working. The patient’s EHR is collected and stored on the cloud which is then easily accessible by doctors. The health industry has always been on top of cyber-attacks. With the onset of the Covid-19 pandemic, there was a sudden surge in telemedicine adoption, remote working and makeshift sites for virus testing and treatment, and under-preparedness, all contributing to new vulnerabilities and giving cybercriminals a new opportunity. In the medical world, we need our medical systems to be secure, reliable, efficient, and should ensure economic data storage and sharing for both patients and medical institutes. Motivated by these facts, we did a review on various MCPS techniques and algorithms that already existed. In this paper, we tried to summarize those techniques and provide a comparative study for the same. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Architectures of cost-effective system for COVID-19 patient monitoring

In this paper are presented a Proof of Concept (POC) architectures of a cost-effective system for COVID-19 patient monitoring. While the hardware used by the system remains the same, two different approaches are shown and compared. This gives freedom and flexibility to hospitals and/or healthcare practitioners to choose with budget and available IT support in mind. Copyright © 2022 The Authors.

Medical Cyber-Physical Systems Enabled with Permissioned Blockchain

Global health security concerns have gained vast importance in recent times with outbreak of COVID-19. Today, the growing interdependence among countries and states has effected into accelerated growth of pandemics. A global need for rugged medical systems on a common platform is deemed today. Pandemics will not stop, they will resurrect again, they will happen irrespective till such times the medical world attains a disease less world in future. But till then, we can attempt to decelerate the pandemics growth enabled with new generation technologies. Medical cyber-physical systems are marred by a number of challenges and this paper proposes a model to negate these identified challenges enabled on multichain blockchain platform that imparts peculiar blockchain characteristics to the network of effected systems. The proposed model also enables to share encrypted data on select blockchain nodes granted defined access controls with proven encryption algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Design and Implementation of OPC UA-Based VR/AR Collaboration Model Using CPS Server for VR Engineering Process

In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories.

A CPS based social distancing measuring model using Edge and Fog computing

Cyber-physical system (CPS) is one of the leading topics for research in academic and industry fields. CPS is an integrated system built with a collection of computation, communication, control, and physical elements to solve real-life problems. Lots of research is going on CPS, but in today’s point of view, the covid-19 is one of the most relevant. Nowadays, COVID -19 has become a headache in our society. Social or physical distancing is one of the most useful non-pharmaceutical interventions (NPI) to minimize virus infections. The regular lifestyle of every human being has been changing rapidly. A contactless lifestyle is becoming a necessity day by day. Society is gradually dependent upon smart technological devices for a contactless lifestyle. In the new-normal lifestyle, many new technologies have been introduced. The government also makes some restrictions on human transmission. However, maintaining social distancing is one of the main challenges of our society. There is no such model that effectively helps people to maintain physical distancing. This paper highlights a framework that will guide maintaining physical distance in a social gathering. The proposed CPS-based model is entirely deployed on Edge and Fog computing architecture. The proposed model calculates the distance between all paired edge devices owned by human beings and informs the user whether the location is safe or not. This Fog and Edge-based model improves the latency and network usage compared to the Cloud computing module.

Healthcare Data Security Using Lightweight Protocol for Cyber Physical System

Healthcare systems are equipped with the latest technological advancement and remotely diagnose the patients. In critical conditions, the patients need continuous monitoring by health experts, which is almost impossible in many cases—for example- in the recent COVID-19 crisis when the hospitals are full of infected people. The advanced cyber-physical system (CPS) based medical devices supplement this monitoring system. Health specialists can connect with patients remotely and receive updated health reports simultaneously using Internet-enabled CPS devices. Due to the openness of security protocols, transferring information in the CPS module is a challenging task. Securing health data, on the other hand, is critical. Existing data security techniques, such as RSA and DSA, have drawbacks;one of the most prominent drawbacks of all existing data security strategies is a lack of resources. This study proposed a lightweight data security technique for sharing information in real-time to address this problem. The proposed approach is generalized, as it will work with all categories of data and provide security to the critical information of healthcare data. Additionally, the model is tested with the cross-platform dataset of different categories like.txt, .pdf, .doc, .png, etc., and found promising outcomes. IEEE

Applying Digital Twin Technology in Higher Education: An Automation Line Case Study

Production systems are being expanded to include Digital Twins (DTs) as part of increased industrial digitalization. DTs can bring benefits e.g., increase visibility, safety, and accessibility of the system. Further, digital experimentation can reduce time and cost. Though, application of DT technologies involves challenges i.e., model accuracy or errors in transferring data or codes between the DT and the physical twin. Many studies on DTs focus on industrial applications. However, DT technology has potential for implementation of digital labs in education. This aspect of DTs is of rising importance as distance education has increased over the last decade and access to physical laboratories can be restricted due to factors such as the Covid-19 pandemic. Thus, there is a need to study the use of DT technology in higher education. To address this, we investigate possibilities and challenges of applying DT technology in education to conduct industrial-like labs virtually. A case of an automation line, with full scale industrial equipment, based at a research center, is focused. Results emphasize that the application of DT technologies require multi-domain expertise to understand the consequences of every single decision in the design process on every piece of equipment involved, making the modelling process complex and time consuming. Thus, when applied in education, test procedures need to be designed to focus on students' motivation, improved learning and understanding of production systems. DTs are considered enabling technologies supporting the concept of Industry 5.0, thus stressing the human-centric aspects of advancing Industry 4.0. The predicted application of DTs emphasizes the need for educational curricula that include laboratory applications and theoretic understanding of DT technologies. This study focusses the application of DT technologies in higher education curricula, but the result of the study can contribute to other areas such as automation and virtual commissioning towards smarter manufacturing. © 2022 The authors and IOS Press.

Towards Adaptive Quality Assurance in Industrial Applications

We propose the AQUILA framework (Adaptive Quality Assurance in Industrial Applications), a concept for digitalization in Industry 4.0 to support the entire industrial manufacturing chain, laying the groundwork for adaptive quality assurance in times of disrupted supply chains and, due to the COVID-19 pandemic, restricted travel possibilities. To that end, our proposed framework allows for the definition and description of industrial processes, quality assurance and testing protocols, and training scenarios in a comprehensive notation based on BPMN, and supports users in task execution, documentation, and evaluation by providing smart glass-based HCI with eye tracking technology, delivering a combination of process documentation, context-sensitive AR visualization, gaze-based interaction schemes, and remote maintenance and assistance functionality. © 2022 IEEE.

MIST-based Tuning of Cyber-Physical Systems Towards Holistic Healthcare Informatics

The entire world seems shaken and disrupted since the strike of Covid-19 ever since its outbreak towards the end of 2019 and its continued perils. During this unprecedented event of the century, people's health emerged as the most vulnerable and affected area either directly or indirectly by the coronavirus and its new variants. Disrupting almost all spheres of life, patients' health and care systems required timely support from healthcare professionals to provide the needed medical advice on one hand and a prescriptive mechanism to avoid another impending casualty. Similarly, a proactive approach became desirable from the health ministry, pharmaceutical firms, medical insurance companies, and other stakeholders in fine-tuning their offerings to the patients as per the recommender systems. The devices to measure the vitals of a person, became more efficient and ergonomically sound with the advent of wearable gadgets. These devices monitored the physical activities of the user and transferred the vital signals wirelessly to any base computing device and cloud-based repositories. This mechanism, however, was reported to fail in addressing the issues with non-communicating or stand-alone devices that were used by the masses in developing countries including India. If the real-time data could be used from these devices, the healthcare diagnosis and analysis of a patient's medical condition could have taken a progressive dimension with the addition of missing data points. This research thus aims to fill the information gap and proposes a transforming approach towards existing non-communicating devices used to measure the vitals like blood pressure, oxygen level, blood sugar, etc. The proposed MIST-based Cyber-Physical System shall create extensive scalability towards the retrieval of the vital details from the devices which were otherwise captured offline previously and were unused at multiple critical points of healthcare processes. © 2022 IEEE.

SecDH: Security of COVID-19 images based on data hiding with PCA.

Nowadays, image security and copyright protection become challenging, especially after the COVID-19 pandemic. In the paper, we develop SecDH as a medical data hiding scheme, which can guarantee the security and copyright protection of the COVID-19 images. Firstly, the cover image is normalized, which offers high resistance against the geometric attacks. Secondly, the normalized principal component as embedding factor is computed, which are calculated based on principal component analysis (PCA) between cover and mark image. Thirdly, the medical image is invisibly marked with secret mark based on normalized component, redundant discrete wavelet transform (RDWT) and randomized singular value decomposition (RSVD) is introduced. Finally, Arnold cat map scheme employed to ensure the security of the watermarking system. Under the experimental evaluation, our SecDH tool is not only imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.

CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19.

The pandemic of novel Coronavirus Disease 2019 (COVID-19) is widespread all over the world causing serious health problems as well as serious impact on the global economy. Reliable and fast testing of the COVID-19 has been a challenge for researchers and healthcare practitioners. In this work, we present a novel machine learning (ML) integrated X-ray device in Healthcare Cyber-Physical System (H-CPS) or smart healthcare framework (called "CoviLearn") to allow healthcare practitioners to perform automatic initial screening of COVID-19 patients. We propose convolutional neural network (CNN) models of X-ray images integrated into an X-ray device for automatic COVID-19 detection. The proposed CoviLearn device will be useful in detecting if a person is COVID-19 positive or negative by considering the chest X-ray image of individuals. CoviLearn will be useful tool doctors to detect potential COVID-19 infections instantaneously without taking more intrusive healthcare data samples, such as saliva and blood. COVID-19 attacks the endothelium tissues that support respiratory tract, and X-rays images can be used to analyze the health of a patient's lungs. As all healthcare centers have X-ray machines, it could be possible to use proposed CoviLearn X-rays to test for COVID-19 without the especial test kits. Our proposed automated analysis system CoviLearn which has 98.98% accuracy will be able to save valuable time of medical professionals as the X-ray machines come with a drawback as it needed a radiology expert.

Development and Prospect of Launch Vehicle Ground Testing Technology

As world space launch activities have entered an intensive stage, how to effectively improve efficiency, reduce costs, and enhance the ability to go into space while ensuring reliability and safety has become an important factor in measuring space capabilities. The launch vehicle must fly reliably and stably, and send the satellite into the predetermined orbit accurately. Not only is the important role of the systems on the vehicle, but ground testing and launch control also play a vital role in ensuring the success of the launch vehicle mission. The emergence of COVID-19 in early 2020 also challenged the personnel-intensive industrial model. Intelligent, unmanned, efficient, and system will be the dominant model in the future. This paper reviews the development status of the world's launch vehicle test launch technology, analyzes the capabilities and shortcomings of existing test launch technology, and proposes the development trend of future launch vehicle test launch technology based on new technologies emerging from the new round of scientific and technological revolution. The outlook for next-generation test launch system is also presented. Future test launch technologies will highlight the three characteristics of digitalization, networking and intelligence. Digitization lays the foundation for test launch informationization. Its development trend is big data analysis and application, replacing the existing software tools to extract, store, search, share, analyze, and process massive and complex data sets to achieve depth test launch data mining and maximum value. Networking provides a physical carrier for information dissemination. Its development trend is the adoption of Cyber-Physical Systems (CPS), integrated computing, communication, and control. Through networking, ground test transmitting equipment has computing, communication, precise control, remote coordination, autonomy and other functions. Intelligence reflects the level of information application. Its development trend is a new generation of artificial intelligence. According to the requirements of vehicle launch, it could quickly generate data and upload binding. Through intelligent detection methods, it could complete the required operations, inspections and tests before launching, and achieve autonomous vehicle launching. In the future, intelligent cyber-physical fusion system based on big data will become the mainstream direction of rocket vehicle test launch technology, which will further simplify operations, improve efficiency, reduce costs, and achieve the goal of "launch during transport". © 2021 by the International Astronautical Federation (IAF). All rights reserved.

May SARS-CoV-2 be prevented by an indoor air monitoring smart data service?

The COVID-19 pandemic has shown the lack of tools for widely monitoring air quality in indoor public spaces, enabling data-driven decisions in everyday life, as they can play a significant role in abating the propagation of the SARS-CoV-2virus. Even actions as simple as opening doors and windows to ventilate rooms are widely known to be highly effective, and they may be further beneficial if triggered depending on a proper evaluation of indoor air quality levels. However, several online systems currently available on the Web mainly provide theoretical indoor air quality estimations without adequately exploiting IoT-supplied data streams. To tackle such issues, this paper describes a smart service for alerting when the air quality conditions become critical for SARS-COV-2 propagation. It is based on a cyber-physical-social platform, enabled by IoT devices that monitor air quality components, elaborates over the collected samples to infer the risk of SARS-COV-2 propagation. The result of the process alerts enabled users. © 2021 IEEE.

Context-Aware Chatbot Based on Cyber-Physical Sensing for Promoting Serendipitous Face-to-Face Communication

To promote the face-to-face communication reduced by COVID-19, we proposed and implemented a context-aware Slack chatbot based on Cyber-Physical sensing that helps colleagues meet more often in the same place. Our system periodically collects the user’s internal context through Slack (cyber sensing) and uses small BLE beacons distributed to colleagues and beacon scanners installed in a laboratory to sense physical attendance (physical sensing). In addition, the system notifies the user of recommended actions, such as lunch or coffee break, depending on the context determined by the Cyber-Physical sensors. We deployed the proposed system in a laboratory environment and conducted an initial experiment for six weeks. Experimental results confirmed that our system can encourage serendipitous face-to-face communication during periods when the frequency of attending school and going to work dropped due to COVID-19. It was also found that in an environment such as a laboratory, where a certain level of trust has already been established, the openness of the collected information can further motivate users to participate in the system. © 2022, Springer Nature Switzerland AG.