ABSTRACT
Many students all over the world have faced some educational issues due to the Covid-19 epidemic. As a consequence, many educational institutes focused on shifting to an E-learning system. This paper introduces a design and implementation steps of a remotely controlled experiment representing a smart hydro energy storage and irrigation system with monitoring capability using photovoltaic power and the Internet of Things (IoT). The experiment is running within the newly proposed Laboratory Learning Management System (LLMS). The remotely controlled experiment is a smart hydro energy storage and irrigation system, where the stored water during the daytime is used at night for smart irrigation of three different types of plants based on the moisture and temperature, in addition to the amount of water that the user sets for every area. In this experiment, during the daytime, the utilities are feeding from the solar panel and battery, but at night, the utilities are feeding from the battery or the hydro turbine that converts the water potential energy to electric energy. The overall Experiment is controlled using IoT sensors and relays which are connected and driven by the parameters that the user sets and can be communicated with the system using the Internet which allows the system to be proactive and take the needed decision in the right time. The main contribution of this system's experiment is the pumping of underground water in irrigation using a renewable and clean energy source, in addition to controlling the systems using IoT through the proposed LLMS. © 2022 IEEE.
ABSTRACT
The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper presented a novel coronavirus herd immunity optimizer algorithm for the optimal accommodation of SPVDG with BESS in the PDS. The proposed algorithm is centered on the herd immunity approach to combat the COVID-19 virus. The problem formulation is focused on the optimal accommodation of SPVDG and BESS to reduce the power loss and enhance the voltage profile of the PDS. Moreover, voltage limits, maximum current limits, and BESS charge-discharge constraints are validated during the optimization. Moreover, the hourly variation of SPVDG generation and load profile with seasonal impact is examined in this study. IEEE 33 and 69 bus PDSs are tested for the development of the presented work. The suggested algorithm showed its effectiveness and accuracy compared to different optimization techniques. © 2023 TÜBÍTAK.
ABSTRACT
The outbreak of COVID-19 has greatly changed everyone's lifestyle all over the world. One of the best ways to prevent the spread of infections is by washing hands properly. Although a number of hand hygiene monitoring systems have been proposed, they either cannot achieve high accuracy in practice or work only in limited environments such as hospitals. Therefore, a ubiquitous, energy-efficient and highly accurate hand hygiene monitoring system is still lacking. In this paper, we present WashRing—the first smart ring-based handwashing monitoring system. In WashRing, we design a Partially Observable Markov Decision Process (POMDP) based adaptive sampling approach to achieve high energy efficiency. Then, we design an automatic feature extraction scheme based on wavelet scattering and a CNN-LSTM neural network to achieve fine-grained gesture recognition. Finally, we model the handwashing gesture classification as a few-shot learning problem to mitigate the burden of collecting extensive data from five fingers. We collect data from 25 subjects over 2 months and evaluate the system performance on both commercial OURA ring and customized ring. Evaluation results show that WashRing achieves 97.8%accuracy which is 10.2%–15.9%higher than state-of-the-arts. Our adaptive sampling approach reduces energy consumption by 64.2%compared to fixed duty cycle sampling strategies. IEEE
ABSTRACT
Digital Contact tracing with smartphone apps may help control the spread of serious pathogens, such as COVID-19. Such apps typically use peer-to-peer Bluetooth data transfer to record a contact. However, they suffer from low adoption rates, high false alarm contact indications, battery drain, and user privacy concerns. This paper proposes BECT or BEacon-based Contact Tracing, a contact tracing framework using static Bluetooth beacon devices installed in public or private places that periodically broadcast packets to nearby users that are stored as coins. Users that are positively diagnosed submit their coin IDs to a third-party service (e.g., local health authority) which can mark these coins as infected and disseminate them to other users. A match between a user's stored coins and an infected coin implies that the user has come in direct or indirect contact with an infected person. The BECT framework does not expose users' private data and conserves the device battery. We use MATLAB simulations to compare the performance of the BECT framework to phone-phone apps in a restaurant scenario and show that BECT has superior contact tracing performance. We also provide general deployment guidelines. © 2022 IEEE.
ABSTRACT
This paper presents a vibration monitoring system for electrical appliances. This system is based on RFID sensors and edge processing technologies. For long-term monitoring, two different operation modes referred to as standby and active modes are introduced. The difference between the two modes is radio wave radiation times. The standby mode is useful to reduce energy consumption and temperature increase of an RFID reader, and amount of data uploaded to a network. This mode also detects a beginning of a vibration event caused by the motor of an electrical appliance. The standby mode subsequently triggers the active mode. The active mode accurately monitors the vibration event and keeps the measured data only for the active mode. Experiments for monitoring a refrigerator demonstrate that the proposed modes enable efficient vibration detections. This system can prevent unintended COVID-19 vaccine disposals caused by the problematic operation and management of refrigerators. © 2022 IEEE.
ABSTRACT
Remote learning has become increasingly prevalent in the past decade. The current covid climate has provided an exponential boost for concurrent usage with video conferencing, specifically remote learning. For a good learning experience, the system needs to provide consistent performance and responsiveness of all applications in the concurrent use case while also delivering a good battery life to keep students engaged. Additionally, the education PC segment is typically budget conscious and fan-less, with a thermal design power of 6 Watts. Customers prefer to reuse existing chassis, thermal solutions and avoid high-cost bill of material, which comes with power-saving features. In this paper, we present the approach to optimizing video conferencing use case within the constraints of the education PC segment, which comes with the challenge of maintaining perf/watt. We also present pursued optimizations and associated learnings for design, software, and system tuning to enhance performance and battery life and provide a good user experience for video conferencing and concurrent use cases. © 2022 IEEE.
ABSTRACT
The City of Zagreb in Croatia and its surroundings have experienced two strong earthquakes within nine months of 2020. Putting this in the context of the increased workload of healthcare facilities due to Covid-19, the distribution system operator (DSO) is encouraged to look for unconventional solutions such as integrating the battery energy storage system (BESS) to supply healthcare facilities during network fault conditions or other extreme network events. The BESS size and location are determined by optimization model, while the control system of the BESS converter, based on the virtual synchronous machine (VSM) concept, is define to test BESS ability to supply critical consumers in the off-grid mode. The models are tested and verified on several real world situations in Zagreb MV distribution network. Future developments and scenarios are also simulated to verify the robustness of the proposed investment. © 2022 IEEE.
ABSTRACT
Only vaccination can not prevent COVID-19 infection. Social distancing and other preventive measures like - frequent hand washing, wearing a face mask can reduce the rising infection rate of COVID-19. It is not feasible to maintain social distancing and ensure hand sanitization in public places by humans as COVID-19 can affect that person or be contaminated by him/her. An automated social distancing system will play an essential role in maintaining social distance within certain boundaries. An automatic social distancing system called 'COV-SSDS' has been proposed in this work. In COV-SSDS, a person has to disinfect the hands with a sanitizer after being detected by the infrared sensor because the servo motor control door does not open without hand sanitization. If the person does not stand in the proper place, he/she will be notified. A liquid crystal display module has been used to display the number of people in the queue and the occupied slots. An alert generation system to alert the people about occupying the empty slot and a power backup unit was also attached to this system which was not found in previous studies. According to the features, feasibility, maintenance, and cost analysis, 'COV-SSDS' is worthy of the previous works. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic is continuously evolving with drastically changing epidemiological situations which are approached with different decisions: from the reduction of fatalities to even the selection of patients with the highest probability of survival in critical clinical situations. Motivated by this, a battery of mortality prediction models with different performances has been developed to assist physicians and hospital managers. Logistic regression, one of the most popular classifiers within the clinical field, has been chosen as the basis for the generation of our models. Whilst a standard logistic regression only learns a single model focusing on improving accuracy, we propose to extend the possibilities of logistic regression by focusing on sensitivity and specificity. Hence, the log-likelihood function, used to calculate the coefficients in the logistic model, is split into two objective functions: one representing the survivors and the other for the deceased class. A multi-objective optimization process is undertaken on both functions in order to find the Pareto set, composed of models not improved by another model in both objective functions simultaneously. The individual optimization of either sensitivity (deceased patients) or specificity (survivors) criteria may be conflicting objectives because the improvement of one can imply the worsening of the other. Nonetheless, this conflict guarantees the output of a battery of diverse prediction models. Furthermore, a specific methodology for the evaluation of the Pareto models is proposed. As a result, a battery of COVID-19 mortality prediction models is obtained to assist physicians in decision-making for specific epidemiological situations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Context. With 'work from home' policies becoming the norm during the COVID-19 pandemic, videoconferencing apps have soared in popularity, especially on mobile devices. However, mobile devices only have limited energy capacities, and their batteries degrade slightly with each charge/discharge cycle. Goal. With this research we aim at comparing the energy consumption of two Android videoconferencing apps, and studying the impact that different features and settings of these apps have on energy consumption. Method. We conduct an empirical experiment by utilizing as subjects Google Meet and Zoom. We test the impact of multiple factors on the energy consumption: number of call participants, microphone and camera use, and virtual backgrounds. Results. Zoom results to be more energy efficient than Google Meet, albeit only to a small extent. Camera use is the most energy greedy feature, while the use of virtual background only marginally impacts energy consumption. Number of participants affect differently the energy consumption of the apps. As exception, microphone use does not significantly affect energy consumption. Conclusions. Most features of Android videoconferencing apps significantly impact their energy consumption. As implication for users, selecting which features to use can significantly prolong their mobile battery charge. For developers, our results provide empirical evidence on which features are more energy-greedy, and how features can impact differently energy consumption across apps. © 2022 ACM.
ABSTRACT
With the emergence of Covid-19, as more people got affected by the fatal disease, the demand for a device to measure the oxygen content in our body has grown up. Due to unavailability of effective treatments, the outcome for critically ill Covid-19 patients depends on the availability of supportive medical care. In the current scenario of limited resources, it is important to identify patients who require close monitoring and serious care, including supplementary oxygen. The rapid spread of this virus as a global pandemic has brought in prodigious challenges to the healthcare system. Several oximeters are currently available on the market that can be utilized for this purpose. However, because they are powered by batteries, their performance degrades over time as the battery drains. In comparison to the widely utilized IR sensor in pulse oximeters, the MAX sensor employed in the suggested device is better. During the second wave of Covid-19, as more people got affected by this life-threatening illness, India witnessed a surge in the demand for oxygen supply. In light of this, apart from the oximeter, we have also suggested a methodology to construct a DIY oxygen generator that can be made using easily available materials in case of an emergency. Water has a chemical formula H2O which can be broken into its constitutional elements H2 and O2. Water is already rich in hydroxide ions but adding sodium bicarbonate as a catalyst raises the OH-concentration even more, allowing it to be utilized as an electrolyte. This paper aims to design a technique to develop both these devices cost-effectively and reliably. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic has dramatically increased the use of face masks across the world. Aside from physical distancing, they are among the most e?ective protection for healthcare workers and the general population. Face masks are passive devices, however, and cannot alert the user in case of improper ?t or mask degradation. Additionally, face masks are optimally positioned to give unique insight into some personal health metrics. Recognizing this limitation and opportunity, we present FaceBit: an open-source research platform for smart face mask applications. FaceBit's design was informed by need?nding studies with a cohort of health professionals. Small and easily secured into any face mask, FaceBit is accompanied by a mobile application that provides a user interface and facilitates research. It monitors heart rate without skin contact via ballistocardiography, respiration rate via temperature changes, and mask-?t and wear time from pressure signals, all on-device with an energy-e?cient runtime system. FaceBit can harvest energy from breathing, motion, or sunlight to supplement its tiny primary cell battery that alone delivers a battery lifetime of 11 days or more. FaceBit empowers the mobile computing community to jumpstart research in smart face mask sensing and inference, and provides a sustainable, convenient form factor for health management, applicable to COVID-19 frontline workers and beyond. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.