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

If and to What Extent Does Organizational Learning Culture Predict Turnover Intentions of Telecommuting Call Center Agents?

This study focuses on measuring the relationship between organizational learning culture (OLC) and turnover intentions of telecommuting call center agents. Although many studies involve the call center industry from different perspectives, the literature is scant in studies that have assessed the relationship between OLC and turnover intent in telecommuting call center agents. Call centers exist in almost every organization worldwide. Organizations have centralized their customer service process through computerbased technologies allowing call center agents to work from home. In addition, in the post-COVID-19 era, telecommuting has become a permanent option for many call center employees. Indeed, in the call center industry, telecommuting has become an essential part of the business strategy that seeks to attract new and maintain current employees. In the call center industry, learning is a factor that influences job satisfaction and turnover intentions. Specifically, OLC increases job satisfaction and performance in telecommuting call center agents, influencing employees' turnover intentions. The study 's findings indicate that OLC is a needed factor that helps lower turnover intentions of telecommuting call center agents in the United States.

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.

Framing blockchain-integrated digital twins for emergent healthcare management at local and city levels: A proof of concept

It has been witnessed that digital technology has the potential to improve the efficiency of emergent healthcare management in COVID-19, which however has not been widely adopted due to unclear definition and configuration. This research aims to propose a proof of concept of digital twins for emergent healthcare management through configuring the cyber and functional interdependencies of healthcare systems at local and city levels. Critical interdependencies of healthcare systems have been firstly identified at both levels, then the information and associated cyber and functional interdependencies embedded in seven critical hospital information systems (HISs) have been identified and mapped. The proposed conceptual digital twin-based approach has been then developed for information coordination amongst these critical HISs at both local and city levels based on permissioned blockchain to (1) integrate and manage the information from seven critical HISs, and further (2) predict the demands of medical resources according to patient trajectory. A case study has been finally conducted at three hospitals in London during the COVID-19 period, and the results showed that the developed framework of blockchain-integrated digital twins is a promising way to provide more accurate and timely procurement information to decision-makers and can effectively support evidence-based decisions on medical resource allocation in the pandemic. © 2023 ICE Publishing: 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.

Three-Stage Power Supply System Model for a Wearable IoT Device for COVID-19 Patients.

During the current crisis caused by the COVID-19 pandemic, Wearable IoT (WIoT) health devices have become essential resources for remote monitoring of the main physiological signs affected by this disease. As well as sensors, microprocessor, and wireless communication elements are widely studied, the power supply unit has the same importance for the WIoT technology, since the autonomy of the system between recharges is of great importance. This letter presents the design of the power supply system of a WIoT device capable of monitoring oxygen saturation and body temperature, sending the collected data to an IoT platform. The supply system is based on a three-stage block consisting of a rechargeable battery, battery charge controller, and dc voltage converter. The power supply system is designed and implemented as a prototype in order to test performance and efficiency. The results show that the designed block provides a stable supply voltage avoiding energy losses, which makes it an efficient and rapidly developing system.

A Temperature-Modified Dynamic Embedded Topic Model

Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and dataset show that the proposed model - nicknamed DETM-tau after the temperature parameter - has been able to improve the model's perplexity and topic coherence for all datasets.

Interactive Communication Robot Handling Crowd Management and Content Delivery in Museums Employing Crowd Counting

Through the last decade, and particularly after the Covid period (2020 - 2022), crowd counting and localization have attracted much attention of AI researchers due to its potential applicability in crowd monitoring and control, public safety, space design, interactive content delivery etc. Once delivery objectives for a system are envisaged and the premises are fixed, we can always construct manifold technology architecture that delivers the set goals. However, in the Indian context a solution of choice needs to be optimized on frugality and ease of adaptability. In this paper we report an economic and replicable application of crowd counting and interactive content delivery in museums through unbiased knowledge systems embedded in robotic museum assistants. We intend to demonstrate a robotic system that can deliver any gallery content to groups of visitors keeping special focus on the exhibits that are popular choices. Crowd counting is used here to enable the content presentation to a group of choice in an interactive way. There are some market-ready solutions available for interactive gallery demonstration by moveable robots but they require not only huge capital investment but are also of limited use within controlled environments. Our proposed design is to multiplex an existing infrastructure of surveillance system as a smart crowd counting and gallery demonstration system along with crowd management with minimum additional hardware infusion. © 2023 IEEE.

Can Universities Help "Build Back Better” in Education? The Socially Embedded University Responds to the Covid-19 Pandemic

The high impact disruptions in the external environment caused by the Covid-19 pandemic revealed the socially embedded character of many universities around the world. University collaborations with schools during the pandemic suggest that they are institutions open to their external environment, capable of learning from and with their environment, and capable of influencing their external environment, helping to address significant social challenges. Drawing on a non-probabilistic survey administered to a convenience sample of 101 universities in 21 countries, I examine how they built partnerships with schools to sustain educational opportunity during the pandemic. The results are informative of the evolving nature of higher education and its mission. They illustrate the responsiveness of universities to societal needs. The findings show that universities are socially connected to their surrounding context, and that they see themselves as engines of social innovation at a time of great unexpected need. The study found the majority of universities to be engaged with schools supporting education during the pandemic. They see such engagement as part of their mission and strategy, even though they perceive it as challenging. Most of such engagements do not have a formalized "theory of action,” but are evolving as the crisis created by the pandemic itself evolved. While such engagement during the pandemic builds on pre-existing engagements with schools, the response during the pandemic provided an opportunity to integrate different efforts across various units. The majority of the universities in the study had a school of education, and about half have a program of pre-service teacher education and few of the collaborations established during the pandemic were new, most were based on pre-existing collaborations. In two thirds of the cases the collaborations with schools during the pandemic were initiated by University leaders. Most of the collaborations consist of developing alternative delivery channels and supporting teachers in developing new skills to teach remotely. Copyright © 2021 Reimers.

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.

Application of embedded computer and improved genetic algorithm in the strategy of community of human destiny: the development of artificial intelligence in the context of Covid-19.

Through the COVID-19 epidemic in 2020, the society has deeply realized the inevitability and necessity of building a community that shares the future of mankind. In the face of severely complex international trends and domestic and international economic conditions, artificial intelligence plays an important auxiliary role in the regular prevention and management of COVID-19. In order to effectively correspond to the formalized extensional prevention and control theory, it is essential to use coordination models, rule systems, prevention and control mechanisms, and governance landscapes to build artificial intelligence corresponding systems. This article uses a basic genetic algorithm to realize the robot path plan. This mainly includes the establishment of environmental models, the discovery of chromosomes and the determination of coding methods, the selection and design of fitness functions, and related designs. This paper proposes a new adaptive adjustment mode based on the basic genetic algorithm, which improves the selection and mutation operation, and improves the optimization efficiency of the genetic algorithm. Building an artificial intelligence response system may face various technical risks and governance dilemmas. Only by improving the rule system of artificial intelligence, creating an epidemic prevention and control ecology, conserving the public spirit of the whole people, strengthening the governance of the source of crisis, and further improving the new momentum of economic and social development and public safety. The modernization of governance capabilities can better respond to the current complex situation.

Report on the 28th Asia and South Pacific Design Automation Conference

The 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) was held at Miraikan, National Museum of Emerging Science and Innovation, Tokyo, Japan, 16 char6319 January 2023. ASP-DAC, started in 1995, is a high-quality and premium conference on Electronic Design Automation (EDA) like other sister conferences such as Design Automation Conference (DAC);Design, Automation Test in Europe (DATE);International Conference on Computer Aided Design (ICCAD);and Embedded Systems Week (ESWEEK). ASP-DAC 2023 adopted an in-person conference style with online features which is the first time in ASP-DAC. Even though the last two ASP-DAC conferences were held as virtual conferences due to the COVID-19 pandemic, ASPDAC 2023 provided opportunities for face-to-face communication not only at sessions, but also at coffee breaks, banquet, and so on for in-person attendees. Online access mainly for participants who were difficult to physically attend was also provided as much as possible.

Proposed Experimental Design of a Portable COVID-19 Screening Device Using Cough Audio Samples

With the proliferation of COVID-19 cases, it has become indispensable to conceive of innovative solutions to abate the mortality count due to the pandemic. With a steep rise in daily cases, it is a known fact that the current testing capacity is a major hindrance in providing the right healthcare for the individuals. The common methods of detection include swab tests, blood test results, CT scan images, and using cough sounds paired with AI. The unavailability of data for the application of deep learning techniques has proved to be a major issue in the development of deep learning-enabled solutions. In this work, a novel solution of a screening device that is capable of collecting audio samples and utilizing deep learning techniques to predict the probability of an individual to be diagnosed with COVID-19 is proposed. The model is trained on public datasets, which is to be manually examined and processed. Audio features are extracted to create a dataset for the model which will be developed using the TensorFlow framework. The trained model is deployed on an ARM CortexM4 based nRF52840 microcontroller using the lite version of the model. The in-built PDM-based microphone is to be used to capture the audio samples. The captured audio sample will be used as an input for the model for screening. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Augmented Spaces: Introducing a Technology-Supported Home Environment to Improve our Mental Health during "Work from Home” Isolation

During the COVID-19 pandemic many restrictions were implemented to prevent the spread of the disease. These restrictions included working from home (WFH) and self-isolation. However, this situation had a negative impact on our mental health, causing depression and anxiety in many employees around the world. In this context, we hypothesized that our home spaces could become a catalyst of positive emotions through the use of technology-supported home environments, which use cyber-physical systems to reduce mental health symptoms during the lockdown. We used a qualitative approach, through interviews and cultural probes, to understand the experience of people who were forced to work from home during the lockdown. Additionally, we used a design science approach to explore technology-supported solutions that could enhance our home spaces. The result is a system that mixes analog and digital elements to create interactive rooms, which have a positive impact on people's well-being. © 2022 IEEE Computer Society. All rights reserved.

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

Short- and long-term strategies for navigating research-ambivalent organizational cultures besetting embedded researchers.

Many health systems may host embedded researchers (ERs) and provide fiscal resources to encourage health service research. However, ERs may remain challenged to initiate research in these settings. This discussion examines how health system culture may impede research initiation, thereby exposing a paradox for embedded researchers immersed in research-ambivalent health systems. The discussion ultimately describes potential short-term and long-term strategies embedded researchers may employ to initiate scholarly inquiry in research-ambivalent health systems.

Training the next generation of learning health system scientists.

Introduction: The learning health system (LHS) aligns science, informatics, incentives, stakeholders, and culture for continuous improvement and innovation. The Agency for Healthcare Research and Quality and the Patient-Centered Outcomes Research Institute designed a K12 initiative to grow the number of LHS scientists. We describe approaches developed by 11 funded centers of excellence (COEs) to promote partnerships between scholars and health system leaders and to provide mentored research training. Methods: Since 2018, the COEs have enlisted faculty, secured institutional resources, partnered with health systems, developed and implemented curricula, recruited scholars, and provided mentored training. Program directors for each COE provided descriptive data on program context, scholar characteristics, stakeholder engagement, scholar experiences with health system partnerships, roles following program completion, and key training challenges. Results: To date, the 11 COEs have partnered with health systems to train 110 scholars. Nine (82%) programs partner with a Veterans Affairs health system and 9 (82%) partner with safety net providers. Clinically trained scholars (n = 87; 79%) include 70 physicians and 17 scholars in other clinical disciplines. Non-clinicians (n = 29; 26%) represent diverse fields, dominated by population health sciences. Stakeholder engagement helps scholars understand health system and patient/family needs and priorities, enabling opportunities to conduct embedded research, improve outcomes, and grow skills in translating research methods and findings into practice. Challenges include supporting scholars through roadblocks that threaten to derail projects during their limited program time, ranging from delays in access to data to COVID-19-related impediments and shifts in organizational priorities. Conclusions: Four years into this novel training program, there is evidence of scholars' accomplishments, both in traditional academic terms and in terms of moving along career trajectories that hold the potential to lead and accelerate transformational health system change. Future LHS training efforts should focus on sustainability, including organizational support for scholar activities.

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.

IoT-Based E-Parking System for Multiplexes and Shopping Malls

This paper helps in automating process of car parking in shopping malls. It helps in making parking more efficient by burning of less fuel. This system is useful for places with large number of people considering less people-to-people contact considering Covid Pandemic and making a safe system for minimal infection transmission from people to people. This paper aims at developing a IoT-based E-parking system. This project uses Micro-controller (ATtiny85) for controlling of sensors. Set of multiple ultrasonic sensors are put on ceilings per floor with multiple slots for detection of vehicles in parked spaces with threshold set for cars. Multiple Wi-Fi modules are used for wirelessly uploading the values of vehicles parked in different floors to cloud from where the Wi-Fi module at entrance extracts data and displays on central display at entrance for assigning empty parking slots to new vehicles on arrival. Entrance display displays number of empty slots on every floor to new customer entering mall parking system. This project achieved objective of making a system which can be used in times of Covid-19 for better safety of people. This paper has been able to achieve its main objectives of making a safe, affordable, scalable parking system which can be used in shopping malls and multiplexes. It can be scaled to large usable parking systems using better sensors and better computing devices. It can provide means of work or business to youth of city for building and selling smart vehicle parking systems and deploy them to multiple malls and multiplexes using help from staff and sell at affordable rates. It can also help make more customizable and modular smart parking systems tailored to use of system in any buildings. Arduino IDE has been used for uploading code to cloud modules in project. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

IoT-Based Wearable and Smart Health Device Solutions for Capnography: Analysis and Perspectives

The potential of the Internet of Health Things (IoHT), also identified in the literature as the Internet of Medical Things (IoMT), is enormous, since it can generate expressive impacts on healthcare devices, such as the capnograph. When applied to mechanical ventilation, it provides essential healthcare to the patient and helps save lives. This survey elaborates on a deep review of related literature about the most robust and effective innovative healthcare solutions using modern technologies, such as the Internet of Things (IoT), cloud computing, Blynk, Bluetooth Low Energy, Robotics, and embedded systems. It emphasizes that IoT-based wearable and smart devices that work as integrated systems can be a faster response to other pandemic crises, respiratory diseases, and other problems that may occur in the future. It may also extend the performance of e-Health platforms used as monitoring systems. Therefore, this paper considers the state of the art to substantiate research about sensors, highlighting the relevance of new studies, strategies, approaches, and novelties in the field.