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.

SignalR based Real-Time applications in education

Before Covid, we introduced our own classroom response system to improve the effectiveness of our teaching. To this end, we adopted an open-source technique, SignalR, which provides a framework for building real-time web applications. Overnight, due to the emergency situation starting in 2019, education was moved to the virtual space. Both students and professors had to learn how to teach or learn using only online facilities, without a testing period. During the emergency, a synchronous online teaching mode was required by our university, so the choice was made to use Microsoft Teams, implemented with SignalR for real-time functionality. After the emergency, we were all happy to have our 'old life' back and return to our personal teaching style, but is it possible, is it possible to continue teaching in the same way as before Covid-19 - is it possible to step into the same river twice? Students have become accustomed to convenient, modern, digital options during the online education period and now that we are back in school, they insist that we continue to use the new tools. In this essay, we want to describe the changes in students' attitudes that we can usefully build on in the future and that will influence the further development of our project. © 2023 IEEE.

Optimal Path Control of a Patient for Rapid Infection Testing

In this paper, we propose a path control framework for guiding and simulating the patient's path of travel to speed up virus testing in pandemic situations, such as COVID-19. We use geographic information and hospital state information to construct graphs to yield optimal travel paths. Pathfinding algorithms A* and Navigation mesh, which have been widely used, are efficient when applied to control agents in a virtual environment. However, they are not suitable for real-time changing cases such as the COVID-19 environment because they guide only predetermined static routes. In order to receive a virus infection test quickly, there are many factors to consider, such as road traffic conditions, hospital size, number of patient movements, and patient processing time, in addition to guiding the shortest distance. In this paper, we propose a framework for digitally twinning various situations by modeling optimization functions considering various environmental factors in real-world urban maps to handle viral infection tests quickly and efficiently.

Obstruction simulation in real-time 3D audio on edge systems

After the COVID-induced lock-downs, augmented/virtual reality turned from leisure to desired reality. Real-time 3D audio is a crucial enabler for these technologies. Nevertheless, systems offering object spatialization in 3D audio fall in two limited cases. They either require long-running pre-renders or involve powerful computing platforms. Furthermore, they mainly focus on active audio sources, while humans rely on the sound's interactions with passive obstructions to sense their environment. We propose a hardware co-processor for real-time 3D audio spatialization supporting passive obstructions. Our solution attains similar latency w.r.t. workstations while draining a tenth of the power, making it suitable for embedded applications. © 2022 IEEE.

Building a Real-Time System to Monitor Students Electronically Based on Digital Images of Face Movement

The spread of the Corona Virus pandemic on a global scale had a great impact on the trend towards e-learning. In the virtual exams the student can take his exams online without any papers, in addition to the correction and electronic monitoring of the exams. Tests are supervised and controlled by a camera and proven cheat-checking tools. This technology has opened the doors of academic institutions for distance learning to be wide spread without any problems at all. In this paper, a proposed model was built by linking a computer network using a server/client model because it is a system that distributes tasks between the two. The main computer that acts as a server (exam observer) is connected to a group of sub-computers (students) who are being tested and these devices are considered the set of clients. The proposed student face recognition system is run on each computer (client) in order to identify and verify the identity of the student. When another face is detected, the program sends a warning signal to the server. Thus, the concerned student is alerted. This mechanism helps examinees reduce cheating cases in early time. The results obtained from the face recognition showed high accuracy despite the large number of students' faces. The performance speed was in line with the test performance requirements, handling 1,081 real photos and adding 960 photos. © 2022 IEEE.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.

Digital Twins-Based Internet of Robotic Things for Remote Health Monitoring of COVID-19 Patients

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author

Development of real-time RT-PCR systems for detection and quantitation of bovine enteric viral pathogens

The enteric viruses in animals are responsible for severe and devastating losses to the livestock owners with a profound negative impact on animal, health, welfare, and productivity. These viruses are usually transmitted via the feco-oral route and primarily infect the digestive tract of the humans, bovines and different mammals as well as birds. Some of the important enteric viruses in ruminants are: Rotavirus A (RVA), Peste des petits virus (PPRV), Norovirus (NV), Bovine corona virus (BoCV) and Bluetongue virus (BTV). In the present study, sensitive, specific and reliable TaqMan probe-based RT-qPCRs were developed and standardized for the rapid detection and quantification of enteric viruses from fecal samples. The assays result in efficient amplification of the RVA, BTV and BoCV RNA with a limit of detection (LoD) of 5, 5 and 4 copies, respectively, which is 1000 times more sensitive than the traditional gel-based RT-PCR. The reproducibility of each assay was satisfactory, thus allowing for a sensitive and accurate measurement of the viral RNA load in clinical samples. In conclusion, real time PCR developed for these viruses are highly specific and sensitive technique for the detection of diarrheic viral pathogens of cattle and buffalo. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

COUNTERSAVIOR: AIoMT and IIoT-Enabled Adaptive Virus Outbreak Discovery Framework for Healthcare Informatics

In the current pandemic, global issues have caused health issues as well as economic downturns. At the beginning of every novel virus outbreak, lockdown is the best possible weapon to reduce the virus spread and save human life as the medical diagnosis followed by treatment and clinical approval takes significant time. The proposed COUNTERSAVIOR system aims at an Artificial Intelligence of Medical Things (AIoMT), and an edge line computing enabled and Big data analytics supported tracing and tracking approach that consumes global positioning system (GPS) spatiotemporal data. COUNTERSAVIOR will be a better scientific tool to handle any virus outbreak. The proposed research discovers the prospect of applying an individual's mobility to label mobility streams and forecast a virus such as COVID-19 pandemic transmission. The proposed system is the extension of the previously proposed COUNTERACT system. The proposed system can also identify the alternative saviour path concerning the confirmed subject's cross-path using GPS data to avoid the possibility of infections. In the undertaken study, dynamic meta direct and indirect transmission, meta behavior, and meta transmission saviour models are presented. In conducted experiments, the machine learning and deep learning methodologies have been used with the recorded historical location data for forecasting the behavior patterns of confirmed and suspected individuals and a robust comparative analysis is also presented. The proposed system produces a report specifying people that have been exposed to the virus and notifying users about available pandemic saviour paths. In the end, we have represented 3-D tracker movements of individuals, 3-D contact analysis of COVID-19 and suspected individuals for 24 h, forecasting and risk classification of COVID-19, suspected and safe individuals.

Real-time Control of the Air Volume in Ventilation Facilities by Limiting Concentration with Cluster Algorithms

Ventilation improves indoor air quality and reduces airborne infections. It is particularly important at present because of the COVID-19 pandemic. Commercially available ventilation facilities can only be instantly turned on/off or at a set time with adjustable air volumes (high, middle, and low). However, maintaining the indoor carbon dioxide concentration while reducing the energy consumption of these facilities is challenging. Hence, this study developed clustering algorithms to determine the carbon dioxide concentration limit thus enabling real-time air volume adjustment. These limit values were set using the existing energy recovery ventilation (ERV) controller. In the experiment, dual estimation was adopted, and the constructing building energy models from data were sampled at a low rate to compare that the ventilation facilities are only turned on/off. In addition, switching control is closely related to fuzzy control;that is, fuzzy control can be considered a smooth version of switching control. The experimental results indicated that the limits of 600 and 700 ppm were suitable to effectively control the real-time air volume based on the ERV operation. An ERV-based carbon dioxide concentration limit reduced the energy consumption of ventilation facilities by 11%implications of this study. IEEE

Deep Reinforcement Learning-Based Approach for Efficient and Reliable Droplet Routing on MEDA Biochips

The micro-electrode-dot-array (MEDA) architecture provides precise droplet control and real-time sensing in digital microfluidic biochips. Previous work has shown that trapped charge under microelectrodes (MCs) leads to droplets being stuck and failures in fluidic operations. A recent approach utilizes real-time sensing of MC health status, and attempts to avoid degraded electrodes during droplet routing. However, the problem with this solution is that the computational complexity is unacceptable for MEDA biochips of realistic size. Consequently, in this work, we introduce a deep reinforcement learning (DRL)-based approach to bypass degraded electrodes and enhance the reliability of routing. The DRL model utilizes the information of health sensing in real time to proactively reduce the likelihood of charge trapping and avoid using degraded MCs. Simulation results show that our approach provides effective routing strategies for COVID-19 testing protocols. We also validate our DRL-based approach using fabricated prototype biochips. Experimental results show that the developed DRL model completed the routing tasks using a fewer number of clock cycles and shorter total execution time, compared with a baseline routing method. Moreover, our DRL-based approach provides reliable routing strategies even in the presence of degraded electrodes. Our experimental results show that the proposed DRL-based routing is robust to occurrences of electrode faults, as well as increases the lifetime and usability of microfluidic biochips compared to existing strategies.

Pose Estimation of Yoga Poses using ML Techniques

POSE ESTIMATION is a technique to identify joints in a human body from an image or video given as input to a computer. Pose estimation can be performed using Machine Learning (ML) techniques and Deep Learning techniques. Lately, it has been receiving lots of attention in the fields of Human Sensing and Artificial Intelligence. The main aim of pose estimation is to predict the poses of humans by locating key points like elbows, knees, wrists etc.In this work, we have proposed a model which uses Mediapipe, an ML framework, to obtain key point coordinates and ML algorithms like SVM, Gaussian Naive Bayes, Random Forest, Gradient Boost and K Neighbours classifier, which are compared and used to predict Yoga poses. Yoga is practised by people of all ages alike these days to fight issues caused both physically and mentally, thus improving the overall quality of life. Especially since the rise of the COVID-19 pandemic, the number of people practising yoga has only been increasing. In the model, human joint coordinates obtained are used as features. The model with the best accuracy and f score (MediaPipe+ SVM) is chosen for the final work.The yoga poses we used are Plank, Warrior 2, Downdog, Goddess, Tree and Cobra. On implementing the work, a real-time video feed from the webcam of the user's system is obtained, and pose estimation and classification of the yoga pose are done. Unlike in most current systems, suggestive measures to correct the yoga posture are also displayed in real-time alongside the webcam display of the person performing yoga along with some other basic pose information. © 2022 IEEE.

A study of Real-Time Non-invasive Detection Techniques to Identify COVID-19 contagious person

The whole world has witnessed the global pandemic situation caused and hampered very badly due to COVID-19. We had seen the adverse effect globally, in terms of health, economy, social lifestyle. So, it's an urgent need to find a rapid detection technique/test to avoid the spread of the virus. The most effective and world-wide accepted detection method of COVID-19 is the RT-PCR. But due to its slow detection time and False-negative rates, researchers and scientists are trying different detection methods such as use of GC-MS, E-nose, Electrochemical method, use of nanomaterial-based sensor arrays. But all these have limitations in terms of real time sensing, detection time, sample preparation, etc. In order to overcome said drawbacks and to get real-time analysis, we are proposing a concept for COVID-19 detection based on the reported literature. As per recent advancement researchers have evident the presence of VOCs in COVID-19 infected person's breath by GC-MS method. A real time system is very much necessary to detect the VOCs in the Exhaled breath of the COVID-19 infected person to minimize the burden of healthcare system. In this article we will discuss and propose the probable detection techniques for real time sensing of the VOCs presence in the Exhaled breath of the COVID-19 infected person. © Published under licence by IOP Publishing Ltd.

Covid-19 Contactless Remedies for Students in Educational Institutes

As we all know fingerprint recognition is one of the secure and accurate Biometric Technologies. If think about it in deep even with the Biometric system the virus can be spread during these situations. To overcome this, we need to come up with a secure and contactless way of authentication. So, let's update to some contactless remedies like Iris authentication which are unique for every individual and they don't need to have any physical contact. So, we can use this Iris detection for a secure and contactless authentication system. The main aim of this research is to provide contactless remedies for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. Coming to the outline of the attendance management system, we will first collect the data from the Kaggle repository. Next, we split the data into training and testing, then we will train the data using transfer learning techniques and test the model by using test data. Finally, we integrated the trained model with the flask. If the Iris matches then the attendance of a particular person will be posted. If not matched then we train the model by adding new person's data. For the construction of modern electronic security systems, real-time face recognition is crucial. Face detection, feature extraction, and face recognition are the three procedures involved. After recognizing the face, it will check whether the person's face matches the collected database. If it matches it will show the person's name, the number of books he took, and what those books are for Library transactions and in the same way the locker will be open if the person's data is matched. The proposed methods are secure and unique contactless ways of authentication for every individual. So, we can use these detection and authentication systems for secure and contactless applications. It can be successfully used for students in Educational institutes like Smart Locking system, Attendance management system, and Library Transaction by using their Iris authentication and Face Recognition. The Covid-19 infection in society will undoubtedly decline if the proposed argument is implemented. © 2023 The authors and IOS Press.

Enhancing Experiential Learning to Address Loss of Hands-on Laboratory Due to Covid-19 Pandemic

—All sectors of life have been hit hard by the pandemic caused by covid-19. This impact has been brutal on streams like engineering in the educational sector, where practical or laboratory courses play a vital role in learning. Due to the limitations of online learning, the students could not explore the full extent of hands-on learning in their Fourth semester ARM microcontroller course. In order to compensate for the loss, the laboratory activities in the Fifth semester RTOS (Real-Time Operating System) Laboratory course are enhanced to provide students with a hands-on learning experience in building applications in both the ARM and RTOS environment. The extended activities enhanced student learning in the ARM environment, which they were previously deprived of during online instruction. Also, emphasis is provided on applying optimization techniques to memory and timing requirements for a given problem statement. The results show that the extended activities helped students co-relate and integrate concepts addressed during their 4th and 5th-semester courses and build a small application of the embedded systems. © 2022, Rajarambapu Institute Of Technology. All rights reserved.

Big Data Analytics Based Recommender System for Tele-Communication Industry

In recent times, the amount of data sent and received through wireless networks has grown quickly. Smartphones and the growth of Internet access around the world are two big reasons for this volume. Due to the current state of global health, which is mostly caused by Covid-19, telecommunications companies have a great chance to find new ways to make money by using Big Data Analytics (BDA) solutions. This is because data traffic has gone up. After all, more customers are using telecommunications services. As most of the world's data is now made by smartphones and sent through the telecom network, telecom operators are facing an information explosion that makes it harder to make decisions based on the data they need to predict how people will act. This problem was solved by making a system that sorts through information and makes suggestions based on how people have behaved in the past. Content-based filtering, collaborative filtering, and a hybrid approach are the three main ways that recommender systems filter data to solve the problem of too much data and give users relevant recommendations based on their interests and the data that is being created in real-time. Distance algorithms like Cosine, Euclidean, Manhattan, and Minkowski are at the heart of the suggested recommender system, which aims to research and design an effective recommendation strategy. The suggested model suggests different telecom packages to meet the needs of users to increase revenue per subscriber and get consumers, telecom providers, and corporations to sign long-term contracts. © 2022 IEEE.

SIGGRAPH Asia 2022-course notes: "become a guardian of al wasl": Developing productions for the world s largest real-Time interactive projection experience

At the time of writing (2021-22), "Become a Guardian of Al Wasl"represented the world's largest interactive experience. Designing, producing and deploying an immersive interactive experience at the monumental scale of the Al Wasl Plaza 360° projection surface necessitated the research, prototyping and testing of proposed solutions, including systems architecture, to meet the scope and specifications of the project. Four problem spaces emerged during development;real-Time rendering, projection-mapping, redundancy, and content synchronisation. In addition, budget constraints, Covid-19, and remote deployment motivated the exploration of other innovative solutions. As there is limited research on the design and development of interactive dome experiences, this paper will present the challenges encountered in developing productions for the world's largest dome display system and in building the underlying real-Time display and support systems. © 2022 Owner/Author.

Improving the Prediction of Passenger Numbers in Public Transit Networks by Combining Short-term Forecasts with Real-time Occupancy Data

Passengers of public transportation nowadays expect reliable and accurate travel information. The need for occupancy information is becoming more prevalent in intelligent public transport systems as people started avoiding overcrowded vehicles during the COVID-19 pandemic. Furthermore, public transportation companies require accurate occupancy forecasts to improve service quality. We present a novel approach to improve the prediction of passenger numbers that enhances a day-ahead prediction with real-time data. We first train a baseline predictor on historical automatic passenger counting data. Next, we train a realtime model on the deviations between baseline prediction and observed values, thus capturing events not addressed by the baseline. For the forecast, we attempt to detect emerging patterns in real time and adjust the baseline prediction with deviations from the patterns. Our experiments with data from Germany show that the proposed model improves the forecast of the baseline model and is only outperformed by artificial neural networks in some instances. If the training sets only cover a limited period of up to four months, our approach outperforms competing methods. For larger training sets, there are mixed results in the sense that for some test cases, certain types of neural networks yield slightly better results, but our method still performs well with less training effort, is explainable along the whole prediction process and can be applied to existing prediction methods. Author

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

The IMU Augment SLAM on Unmanned Vehicle for Detection of Protective Measures in COVID-19

Detecting protective measures (e.g., masks, goggles and protective clothing) is a momentous step in the fight against COVID-19. The detection mode of unmanned devices based on Simultaneous localization and mapping (SLAM) and fusion technology is more efficient, economical and safe than the traditional manual detection. In this paper, a tightly-coupled nonlinear optimization approach is used to augment the visual feature extraction of SLAM by the gyroscope of the IMU to obtain a high-precision visual inertial system for joint position and pose estimation. Based on the VINS-Mono frame, first, an LSD algorithm based on a conditional selection strategy is proposed to extract line features efficiently. Then, we propose recovering missing point features from line features. Moreover, we propose a strategy to recover vanishing point features from line features, and add residuals to the SLAM cost function based on optimization, which optimizes point-line features in real time to promote the tracking and matching accuracy. Second, the wavelet threshold denoising method based on the 3σ criterion is used to carry out real-time online denoising for gyroscope to improve the output precision. Our WD-PL-VINS was measured on publicly available EuRoC datasets, TUM VI datasets and evaluated and validated in lab testing with a unmanned vehicle (UV) based on the NVIDIA Jetson-TX2 development board. The results show that our method's APE and RPE on MH-03-easy sequences are improved by 69.28% and 97.66%, respectively, compared with VINS-Mono. © 2001-2012 IEEE.