Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

Methodology to carry out medical meeting online, based on low-cost solutions

One of the effects of the COVID-19 pandemic is the adaptation of most of the activities remotely or virtually, in the case of medical appointments, in the different specialties other than emergencies produced by COVID-19. Most of them continued in the format through online appointments. One of the important processes in medical evaluations is related to the so-called specialist boards, where special cases are evaluated, for which several physicians must be connected online, in addition to being able to make reports jointly. In this paper we develop a methodology to perform medical meetings of specialists, using a platform dedicated to the use in video games, through the DISCORD tool interconnectivity from various devices is performed, the results demonstrate the interactivity and applicability of the methodology, so it can be applied in different processes where interconnectivity between different devices and the concurrence of several users is required. We present a methodology to configure virtual appointment rooms, the results allow us to verify that the methodology can be replicated and scaled according to the needs. © 2022 ACM.

Geographical Dependence of Open Hardware Optimization: Case Study of Solar Photovoltaic Racking

Open-source technological development is well-known for rapid innovation and providing opportunities to reduce costs and thus increase accessibility for a wide range of products. This is done through distributed manufacturing, in which products are produced close to end users. There is anecdotal evidence that these opportunities are heavily geographically dependent, with some locations unable to acquire components to build open hardware at accessible prices because of trade restrictions, tariffs, taxes, or market availability. Supply chain disruptions during the COVID-19 pandemic exacerbated this and forced designers to pivot towards a la carte-style design frameworks for critical system components. To further develop this phenomenon, a case study of free and open-source solar photovoltaic (PV) racking systems is provided. Two similar open-source designs made from different materials are compared in terms of capital costs for their detailed bill of materials throughout ten locations in North, Central and South America. The differences in economic optimization showed that the costs of wood-based racks were superior in North America and in some South American countries, while metal was less costly in Central and South America. The results make it clear that open hardware designs would be best to allow for local optimization based on material availability in all designs. © 2023 by the authors.

Overcoming Chip Shortages: Low-Cost Open-Source Parametric 3-D Printable Solderless SOIC to DIP Breakout Adapters

The COVID-19 pandemic exposed the vulnerability of global supply chains of many products. One area that requires improved supply chain resilience and that is of particular importance to electronic designers is the shortage of basic dual in-line package (DIP) electronic components commonly used for prototyping. This anecdotal observation was investigated as a case study of using additive manufacturing to enforce contact between premade, off-the-shelf conductors to allow for electrical continuity between two arbitrary points by examining data relating to the stock quantity of electronic components, extracted from Digi-Key Electronics. This study applies this concept using an open hardware approach for the design, testing, and use of a simple, parametric, 3-D printable invention that allows for small outline integrated circuit (SOIC) components to be used in DIP package circuits (i.e., breadboards, protoboards, etc.). The additive manufacture breakout board (AMBB) design was developed using two different open-source modelers, OpenSCAD and FreeCAD, to provide reliable and consistent electrical contact between the component and the rest of the circuit and was demonstrated with reusable 8-SOIC to DIP breakout adapters. The three-part design was optimized for manufacturing with RepRap-class fused filament 3-D printers, making the AMBB a prime candidate for use in distributed manufacturing models. The AMBB offers increased flexibility during circuit prototyping by allowing arbitrary connections between the component and prototyping interface as well as superior organization through the ability to color-code different component types. The cost of the AMBB is CAD $0.066/unit, which is a 94% saving compared to conventional PCB-based breakout boards. Use of the AMBB device can provide electronics designers with an increased selection of components for through-hole use by more than a factor of seven. Future development of AMBB devices to allow for low-cost conversion between arbitrary package types provides a path towards more accessible and inclusive electronics design as well as faster prototyping and technical innovation.

A countless variant simulation-based toolkit for remote learning and evaluation

The COVID-19 pandemic has brought about a profound transformation in the educational landscape in recent months. Educators worldwide have been challenged to tackle academic issues they could never have imagined. Among the most stressful situations faced by students and teachers is implementing online assessments. This paper proposes a system that includes exam prototypes for computer architecture modules at the higher education level. This system generates a wide range of questions and variations on the server side, supported by a set of simulators, resulting in many unique examination proposals. This system streamlines the monitoring process for the teacher, as it eliminates the possibility of two students receiving similar exams and reduces student stress by allowing them to practice with a limitless number of exam samples. This paper also highlights several indicators that demonstrate the advantages of this framework. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

DT2CAM: A ecision ree to ontent ddressable emory Framework

Decision trees are powerful tools for data classification. Accelerating the decision tree search is crucial for on-the-edge applications with limited power and latency budget. In this paper, we propose a content-addressable memory compiler for decision tree inference acceleration. We propose a novel ”adaptive-precision”scheme that results in a compact implementation and enables an efficient bijective mapping to ternary content addressable memories while maintaining high inference accuracies. We also develop a resistive-based functional synthesizer to map the decision tree to resistive content addressable memory arrays and perform functional simulations for energy, latency, and accuracy evaluations. We study the decision tree accuracy under hardware non-idealities including device defects, manufacturing variability, and input encoding noise. We test our framework on various decision tree datasets including Give Me Some Credit, Titanic, and COVID-19. Our results reveal up to 42.4%energy savings and up to <inline-formula><tex-math notation="LaTeX">$17.8\times$</tex-math></inline-formula> better energy-delay-area product compared to the state-of-art hardware accelerators, and up to 333 million decisions per sec for the pipelined implementation. IEEE

An Intelligent IoT-based COVID-19 Contact Tracing System for Ashesi University

The outbreak of contagious diseases demand isolation and quarantining of infected persons and people they have been in close proximity with. This can be easily achieved if technology-based systems are designed to facilitate contact tracing. The aim of this research project is to develop a privacy focused IoT-based COVID-19 contact tracing system that leverages mobile devices and artificial intelligence for the Ashesi University community. To achieve this, we divided the project into two main parts: The software sub-system and the hardware subsystem. The software sub-system comprises of a cross-platform mobile application that tracks users, and an admin portal to monitor user activities. The hardware sub-system is an IoT-based system that uses a Raspberry Pi to capture indoor images with the aid of a Raspberry Pi camera module. It processes the images to determine whether the occupants of the room have been in close proximity with one another or not while relaying feedback to them via its actuators and at the same time updates the admin portal. Through system testing, it was identified that 32% our system users considered privacy during the pandemic as critical even though 95% confirmed that the system assures very high level of privacy. © 2022 IEEE.

A Loop-Mediated Isothermal Amplification (Lamp)-Based Point-of-Care System for Rapid on-Site Clinical Detection of Sars-Cov-2 Viruses

In the ongoing COVID-19 pandemic, sensitive and rapid on-site detection of the SARS-CoV-2 coronavirus has been one of crucial objectives. A point-of-care (PoC) device called PATHPOD for quick, on-site detection of SARS-CoV-2 employing a real-time reverse-transcription loop-mediated isothermal amplification (RT-rLAMP) reaction on a polymer cartridge. The PATHPOD consists of a standalone device (weighing under 1.2 kg) and a cartridge, and can identify 10 distinct samples and 2 controls in less than 50 minutes. The PATHPOD PoC system is fabricated and clinically validated for the first time in this work © 2023 IEEE.

Cybersecurity Attacks and Vulnerabilities During COVID-19

As a result of quick transformation to digitalization for providing the employees teleworking/home office services with the capabilities to access company resources from outside the company over Internet using remote desktop and virtual private network (VPN) applications and the increase in digital activity during COVID-19 such as the usage of audio/video conferencing applications, many businesses have been victims of cyber attacks. This paper investigates whether there was an increase in the frequency of cyber attacks during COVID-19. It also identifies the motivations for such attacks in light of software/hardware/system vulnerabilities. Following this research, we also categorize vulnerabilities and develop a taxonomy. Such a taxonomy helped to identify the type of attacks on their frequency and their impact. To do that, we developed a research methodology to collect attack and vulnerability information from the selected databases. Using relevant key words, we developed the taxonomy that led us to create insightful information to answer the research questions that are thoroughly analyzed and presented accordingly. This work also recommended a list of mitigation measures that can be considered in the future to prepare the industry for a similar pandemic including establishing and maintaining a Information Security Management System (ISMS) by following relevant standards (ISO/SAE 2700x, BSI-Standards 200-x, SMEs: CISIS12®). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Remote Visiting and Monitoring System for Neonatal Intensive Care Units via Internet of Things

To meet the visiting needs of families of children in neonatal intensive care unit and reduce the burden of hospital management during the COVID-19 epidemic, we developed a remote visiting and monitoring system using the internet of things. The Raspberry Pi is used as the core hardware platform. The real-time signal of the bedside monitor is converted into a virtual camera, and is connected to the Raspberry Pi which has a real camera with CMOS Serial Interface (CSI). The frames of the two cameras are collected via FFmpeg technology, and then are pushed to the cloud server through Real-Time Messaging Protocol (RTMP). The video streams are then transferred and distributed via a Nginx server running RTMP protocol, and finally are displayed on the web page via the Flask framework. When tested, the system ran stably, and the real-time pictures from the camera and the bedside monitor screen in the hospital were clearly shown on a personal computer or a mobile phone in a remote distance out of the hospital, just by click the link of the associated web page. We think this system is helpful for families to remotely visit the babies anywhere any time, and it is also helpful for hospitals to reduce the human workload and the financial expenditure. © 2022 ACM.

Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper 'FaceGuard: A Wearable System To Avoid Face Touching1.' It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data. © 2022 IEEE.

DeepWare: Imaging Performance Counters With Deep Learning to Detect Ransomware

In the year passed, rarely a month passes without a ransomware incident being published in a newspaper or social media. In addition to the rise in the frequency of ransomware attacks, emerging attacks are very effective as they utilize sophisticated techniques to bypass existing organizational security perimeter. To tackle this issue, this paper presents "DeepWare,” which is a ransomware detection model inspired by deep learning and hardware performance counter (HPC). Different from previous works aiming to check all HPC results returned from a single timing for every running process, DeepWare carries out a simple yet effective concept of " imaging hardware performance counters with deep learning to detect ransomware ,” so as to identify ransomware efficiently and effectively. To be more specific, DeepWare monitors the system-wide change in the distribution of HPC data. By imaging the HPC values and restructuring the conventional CNN model, DeepWare can address HPC's nondeterminism issue by extracting the event-specific and event-wise behavioral features, which allows it to distinguish the ransomware activity from the benign one effectively. The experiment results across ransomware families show that the proposed DeepWare is effective at detecting different classes of ransomware with the 98.6% recall score, which is 84.41%, 60.93%, and 21% improvement over RATAFIA , OC-SVM , and EGB models respectively. DeepWare achieves an average MCC score of 96.8% and nearly zero false-positive rates by using just a 100 ms snapshot of HPC data. This timeliness of DeepWare is critical on the ground that organizations and individuals have the opportunity to take countermeasures in the first stage of the attack. Besides, the experiment conducted on unseen ransomware families such as CoronaVirus, Ryuk, and Dharma demonstrates that DeepWare has excellent potential to be a useful tool for zero-day attack detection.

Machine Learning and Intelligent Network System based Covid 19 Health Care System

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

An Analysis of Public Service Satisfaction of Tourists at Scenic Spots: The Case of Xiamen City

The outbreak of the COVID-19 epidemic has greatly hindered the development of the tourism industry. It is urgent for the city to improve the quality of public service in scenic spots, so as to attract more tourists and achieve sustainable development. With a literature review and reference to some guidance, the evaluation indicator system of public service satisfaction of scenic spots was constructed based on the analytic hierarchy process. Then, we distributed two questionnaires to complete the study. The first is the expert questionnaire for the evaluation indicator system. We used YAAHP software to process the questionnaire data and calculated the weight of each indicator, which provided a basis for the following analysis. The second is the questionnaire distributed to tourists of Xiamen. Then, we used the fuzzy comprehensive evaluation method to analyze the satisfaction of public services in scenic spots. The outcomes show that tourists are overall satisfied with the public services of scenic spots in Xiamen. However, there are still some problems, such as lagging information service, inadequate security, and supervision in the scenic spot. Therefore, the city and scenic spots should improve the level of smart tourism service, strengthen the construction of hardware and software facilities, and focus on the protection of tourists' rights and interests. © 2023 by the authors.

A high-speed unsupervised hardware architecture for rapid diagnosis of COVID-19

In the diagnosis of COVID-19, investigation, analysis, and automatic counting of blood cell clusters are the most essential steps. Currently employed methods for cell segmentation, identification, and counting are time-consuming and sometimes performed manually from sampled blood smears, which is hard and needs the support of an expert laboratory technician. The conventional method for the blood-count-test is by automatic hematology analyzer which is quite expensive and slow. Moreover, most of the unsupervised learning techniques currently available presume the medical practitioner to have a prior knowledge regarding the number and action of possible segments within the image before applying recognition. This assumption fails most often as the severity of the disease gets increased like the advanced stages of COVID-19, lung cancer etc. In this manuscript, a simplified automatic histopathological image analysis technique and its hardware architecture suited for blind segmentation, cell counting, and retrieving the cell parameters like radii, area, and perimeter has been identified not only to speed up but also to ease the process of diagnosis as well as prognosis of COVID-19. This is achieved by combining three algorithms: the K-means algorithm, a novel statistical analysis technique-HIST (histogram separation technique), and an islanding method an improved version of CCA algorithm/blob detection technique. The proposed method is applied to 15 chronic respiratory disease cases of COVID-19 taken from high profile hospital databases. The output in terms of quantitative parameters like PSNR, SSIM, and qualitative analysis clearly reveals the usefulness of this technique in quick cytological evaluation. The proposed high-speed and low-cost architecture gives promising results in terms of performance of 190 MHz clock frequency, which is two times faster than its software implementation. © 2022 John Wiley & Sons Ltd.

Deep CNN Based Video Compression With Lung Ultrasound Sample

Video compression and transmission is an ever-growing area of research with continuous development in both software and hardware domain, especially when it comes to medical field. Lung ultra sound (LUS) is identified as one of the best, inexpensive and harmless option to identify various lung disorders including COVID-19. The paper proposes a model to compress and transfer the LUS sample with high quality and less encoding time than the existing models. Deep convolutional neural network is exploited to work on this, as it focusses on content, more than pixels. Here two deep convolutional neural networks, ie, P(prediction)-net and B(bi-directional)-net model are proposed that takes the input as Prediction, Bidirectional frame of existing Group of Pictures and learn. The network is trained with data set of lung ultrasound sample. The trained network is validated to predict the P, B frame from the GOP. The result is evaluated with 23 raw videos and compared with existing video compression techniques. This also shows that deep learning methods might be a worthwhile endeavor not only for COVID-19, but also in general for lung pathologies. The graph shows that the model outperforms the replacement of block-based prediction algorithm in existing video compression with P-net, B-net for lower bit rates. © The Author('s).

Digital inequalities at high schools in Thailand: A survey-based exploration leading to expert-backed bridging strategies

Purpose: The purpose of this research is to explore the accessibility of online education for high school students in Thailand. It especially focuses on identifying the inequalities among Thai students in terms of availability of IT hardware, internet access, and IT skills by comparing the results of surveys of students in urban areas with those from students in rural areas within Thailand. Based on these findings and input from experts in the Thai education system, strategies to reduce digital inequalities are presented. Design/methodology/approach: To fulfill the objectives of this research, primary data is collected through online surveys with Thai high school students in order to explore digital inequalities. Additionally, semi-structured interviews with experts on Thai education are conducted in order to evaluate the findings from the students' survey responses and to generate bridging strategies. Moreover, a regional comparison based on findings from research conducted in neighboring countries will enable an analysis of the findings in an international context. Findings: This research provides information and insights into digital inequalities existing in the Thai education system. It reveals insights into the availability of IT hardware and access to the internet for online education, as well as the IT skills of high school students across Thailand. It highlights the differences in these areas between urban and rural locations within the country. Based on these findings, expert-backed recommendations are provided to bridge these inequalities. Originality/value: The demand for IT in education is increasing significantly. Recent developments, such as the COVID-19 pandemic, have accelerated such trends. These rapid evolutions need to be explored regularly in order to inform appropriate intervention strategies. Therefore, this research contributes to academia and enhances the ability of stakeholders and decision-makers in Thailand's education sector to respond effectively to the increasing digital inequalities experienced by Thai high school students. © 2022 International Consortium for Electronic Business. All rights reserved.

Student Perspectives on Remote Hardware Labs and Equitable Access in a Post-Pandemic Era

This Research Full Paper builds on a prior study that compared overall student performance between in-hand versus remotely accessible hardware in digital design courses. The COVID-19 pandemic necessitated a global educational shift to emergency online learning that led to rethinking the delivery of engineering labs. The prior study showed that, amidst pandemic-necessitated online learning, student understanding was not impeded by the incorporation of remotely accessible hardware into the course curriculum;rather, using remote hardware resulted in similar or better learning outcomes. In this paper, we analyze the remotely accessible hardware lab through the lens of equity, investigating the student perspective on equitable access and the remote lab experience. The study accomplishes this goal by surveying students of a junior-level digital design course who use a remotely accessible hardware lab for completing their assignments. The survey aims to determine the factors deemed important by today's learners - those who have experienced remote learning for approximately two years of their educational careers - when considering equitable access and remote labs. Survey questions utilized the multiple-choice, semantic differential scale, and Likert scale formats for quantitative analysis as well as inductive coding of freeform responses for qualitative analysis. Initial findings from the survey are the key considerations of the surveyed students which include Factors of the Remote Experience (FREs) and Factors of Equitable Access (FEAs). FREs and FEAs specifically relate to the Student's Access to Electronic Devices, the Student's Environment Outside of Class, the Student's Schedule, the Student's Internet Quality, the lab's Learnability, the lab's Web Interface Design, the lab's Convenience, the lab's Overall Positive Experience, the lab's Ease of Use, the lab's Internet Quality, and the lab's Affordability. Rooted in the online learner's experience, these results contribute to an improved understanding of how students perceive equitable access to engineering education which shall guide better-informed advancements in the field in a post-pandemic world. © 2022 IEEE.

AI-Based COVID Alert System on Embedded Platform

Corona Virus Disease (COVID-19) has hit the world hard and almost every country has faced its consequences may be the population and number of people affected or economically. Crowd management is incredibly tough for big surroundings and continuous watching manually is troublesome to execute. Vaccinated people are also getting affected by the virus so it is advisable to take Public Health & Social Measures (PHSM) such as wearing a proper mask, sanitization and keeping social distancing in crowded places. The proposed paper presents a machine learning based real-time Covid alert and prevention system to ensure Covid appropriate behavior in public places and social gatherings. There are three modules under this system: (i) Real-time Face mask detection, where persons with masks, improper masks or no mask are detected and classified;(ii) Real-time people counting for ensuring a limit on public meetings and social gatherings and (iii) Real-time social distance monitoring. All these modules are integrated and deployed on embedded hardware, NVidia's Jetson Nano. The implementation results are presented and analysis of the detection is done in real-time on the edge-AI platform. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Teaching Practical Robotics During the COVID-19 Pandemic: A Case Study on Regular and Hardware-in-the-Loop Simulations

Laboratory experiments are important pedagogical tools in engineering courses. Restrictions related to the COVID-19 pandemic made it very difficult or impossible for laboratory classes to take place, resulting on a fast transition to simulation as an approach to guarantee the effectiveness of teaching. Simulation environments are powerful tools that can be adopted for remote classes and self-study. With these tools, students can perform experiments and, in some cases, make use of the laboratory facilities from outside of the University. This paper proposes and describes two free tools developed during the COVID-19 pandemic lock-down that allowed students to work from home, namely a set of simulation experiments and a Hardware-in-the-loop simulator, accessible 24/7. Two approaches in Python and C languages are presented, both in the context of Robotics courses for Engineering students. Successful results and student feedback indicate the effectiveness of the proposed approaches in institutions in Portugal and in the Netherlands. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.