ABSTRACT
Given the current coronavirus (COVID-19) situation around the world, we may have to face a long-term battle with coronavirus. It is necessary to prepare and stay resilient with some other techniques to improve air quality in buildings, especially in clinics and hospitals. In this paper, we have developed Ultraviolet-C (UVC) light-emitting diode (LED) modules which can be implemented in air ducts in heating, ventilation, and air conditioning system for airborne disinfection. An LED module is designed with LED panels as the basic unit so that it is easy to scale up to accommodate for air ducts with different sizes. Both experiments and simulations are carried out to study its disinfection performance. The results show that more than 76% and 85% of the pathogen can be inactivated within 60 and 90 min, respectively, in a meeting room with a volume of 107 m3 by using one LED module. Simulations for two LED modules show that the disinfection efficacy is more than two times compared to that of one LED module. In addition to the pathogen used in the experiments, the disinfection performance of the LED module for inactivation of SARS-CoV-2 virus based on the literature is investigated numerically. It shows that more than 99.70% of pathogens receive UV dose larger than 4.47 J/m2, leading to an almost 89.10% disinfection rate for SARS-CoV-2 virus within one hour using the two LED modules in the same meeting room. © 2023 Author(s).
ABSTRACT
In the present scenario like COVID-19 pandemic, to maintain physical distance, the gait-based biometric is a must. Human gait identification is a very difficult process, but it is a suitable distance biometric that also gives good results at low resolution conditions even with face features that are not clear. This study describes the construction of a smart carpet that measures ground response force (GRF) and spatio-temporal gait parameters (STGP) using a polymer optical fiber sensor (POFS). The suggested carpet contains two light detection units for acquiring signals. Each unit obtains response from 10 nearby sensors. There are 20 intensity deviation sensors on a fiber. Light-emitting diodes (LED) are triggered successively, using the multiplexing approach that is being employed. Multiplexing is dependent on coupling among the LED and POFS sections. Results of walking experiments performed on the smart carpet suggested that certain parameters, including step length, stride length, cadence, and stance time, might be used to estimate the GRF and STGP. The results enable the detection of gait, including the swing phase, stance, stance length, and double supporting periods. The suggested carpet is dependable, reasonably priced equipment for gait acquisition in a variety of applications. Using the sensor data, gait recognition is performed using genetic algorithm (GA) and particle swarm optimization (PSO) technique. GA- and PSO-based gait template analyses are performed to extract the features with respect to the gait signals obtained from polymer optical gait sensors (POGS). The techniques used for classification of the obtained signals are random forest (RF) and support vector machine (SVM). The accuracy, sensitivity, and specificity results are obtained using SVM classifier and RF classifier. The results obtained using both classifiers are compared. © 2023 Mamidipaka Hema et al.
ABSTRACT
In the last year the market of ultraviolet (UV) light-emitting diodes (LEDs) had a huge increase due to the higher demand of devices caused by the pandemic crisis. In fact, it was demonstrated that UV LEDs, and in particular UV-B and UV-C LEDs, could be used as efficient sources for the disinfection of surfaces against Sars-CoV-2. In this work, we investigated electrical, optical and spectral degradation mechanisms in a series of commercial UV-C LEDs (275 nm - 280 nm) available on market. We present an exhaustive comparison of the main sample characteristics, studying their evolution when the LEDs are submitted to constant current stress tests (for about 350 h) at the absolute maximum current indicated in the respective datasheets. In particular, we carried out an extensive set of measurements, including current-voltage (I-V), optical power-current (L-I) and power spectral density (PSD) characteristics at various steps of stress, combined with the analysis of the drive voltage during the tests and of the thermal droop of the devices. We found: (i) a set of LEDs with a promising L80 of 10000 min (about 170 h), (ii) the presence of parasitic peaks and bands in all devices, ascribed to radiative recombination outside the QWs, and (iii) a substantial increase in thermal droop in all LEDs due to a decrease in injection efficiency and an increase in SRH recombination events during the stress tests. © 2022 SPIE. All rights reserved.
ABSTRACT
The blood oxygen saturation level (SpO2) has become one of the vital body parameters for the early detection, monitoring, and tracking of the symptoms of coronavirus diseases 2019 (COVID-19) and is clinically accepted for patient care and diagnostics. Pulse oximetry provides non-invasive SpO2 monitoring at home and ICUs without the need of a physician/doctor. However, the accuracy of SpO2 estimation in wearable pulse oximeters remains a challenge due to various non-idealities. We propose a method to improve the estimation accuracy by denoising the red and IR signals, detecting the signal quality, and providing feedback to hardware to adjust the signal chain parameters like LED current or transimpedance amplifier gain and enhance the signal quality. SpO2 is calculated using the red and infrared photoplethysmogram (PPG) signals acquired from the wrist using Texas Instruments AFE4950EVM. We introduce the green PPG signal as a reference to obtain the window size of the moving average filter for baseline wander removal and as a timing reference for peak and valley detection in the red and infrared PPG signals. We propose the improved peak and valley detection algorithm based on the incremental merge segmentation algorithm. Kurtosis, entropy, and Signal-to-noise ratio (SNR) are used as signal quality parameters, and SNR is further related to the variance in the SpO2 measurement. A closed-loop implementation is performed to enhance signal quality based on the signal quality parameters of the recorded PPG signals. The proposed algorithm aims to estimate SpO2 with a variance of 1% for the pulse oximetry devices. IEEE
ABSTRACT
Photoplethysmography (PPG) is an optical technique that monitors oxygen saturation levels that is captured by pulse oximeters and some wearables such as smartwatches. The technique has been shown to overestimate oxyhemoglobin saturation in patients with darker skin, potentially leading to silent hypoxia and a disproportionately higher number of deaths in black and brown COVID-19 patients. We demonstrate a novel PPG technique that uses radially polarized light created by light-emitting diodes (LEDs) to address this problem. Our method performs single-shot, multiple polarization measurements using a single wavelength. We present a new use for vector-beams as well as the first demonstration of vector-beam generation using LEDs. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.
ABSTRACT
Modern life-style is creating an indoor generation: human beings spend approximately 90% of their time indoors, almost 70% of which is at home – this trend is now exacerbated by the lockdowns/restrictions imposed due to the COVID-19 pandemic. That large amount of time spent indoors may have negative consequences on health and well-being. Indeed, poor indoor air quality is linked to a condition known as sick building syndrome. Therefore, breathing the freshest air possible is of outmost importance. Still, due to reduced ventilation rates, indoor air quality can be considerably worse than outdoor. Heating, ventilation, and air conditioning (HVAC), air filtration systems and a well-ventilated space are a partial answer. However, these approaches involve only a physical removal. The photocatalytic mineralization of pollutants into non-hazardous, or at least less dangerous compounds, is a more viable solution for their removal. Titanium dioxide, the archetype photocatalytic material, needs UVA light to be ‘activated’. However, modern household light emitting diode lamps irradiate only in the visible region of the solar spectrum. We show that the surface of titanium dioxide nanoparticles modified with copper oxide(s) and graphene has promise as a viable way to remove gaseous pollutants (benzene and nitrogen oxides) using a common light emitting diode bulb, mimicking real indoor lighting conditions. Titanium dioxide, modified with 1 mol% CuxO and 1 wt% graphene, proved to have a stable photocatalytic degradation rate, three times higher than that of unmodified titania. Materials produced in this research work are thus strong candidates for offering a safer indoor environment. © 2022 Elsevier Ltd
ABSTRACT
AlGaN germicidal ultraviolet (GUV) light emitting diodes (LEDs) are one of the most promising disinfection technologies in fighting the COVID-19 pandemic;however, GUV LEDs are still lacking in efficiency due to low p-type doping efficiency in p-AlGaN. The most successful approach for producing conductive p-type AlGaN is the implementation of a polarization-enhanced short period AlxGa1-xN/ AlyGa1-yN superlattice (SL) structure, which enhances hole injection and reduces device operating voltage. In this report, we investigated different aspects of the superlattice including the AlxGa1-xN and AlyGa1-yN alloy constituent compositions, x and y, period thickness, total thickness, and Mg dopant concentration in terms of LED performance as well as electrical, optical, and morphological characteristics. The polarization-enhanced p-type doping in the AlGaN superlattice was also investigated computationally, giving excellent agreement with experimental results. Highly efficient UVC LEDs (279 nm) with EQE of 2% at 5 A/cm2 were demonstrated. A maximum output power of 5.5 mW (56 mW/mm2) was achieved at 100 mA. IEEE
ABSTRACT
Conventional streetlight’s constant need for high power and the ill effects it has spawned on the environmental ecosystem has led researchers to adopt the idea of smart lights in order to minimize energy consumption and maximize power efficiency. This paper proposes S-LIGHT, which is a PWM-based LED adaptive light controlling system that can be deployed at public parks and other outdoor recreational venues, which applies intelligent illumination control of an LED lights. The design is based on Pulse Width Modulation technique which optimizes the overall power consumption and simultaneously supporting a multi-functional and user-friendly post. Smart street lighting aims to make cities feel safer at night, make lights more efficient, and substantially reduce costs of maintenance and energy by integrating sensors and alternative technologies to automate the light. S-LIGHT uses an Arduino UNO board along with a Passive Infrared (PIR) sensor to swiftly increase the brightness of the high-power LED light during the night in the presence of human motion, and a Light Dependent Resistor (LDR) sensor to turn on/off the light by adapting itself to the time of night/day. S-LIGHT also provides a multi-functional post that supports an emergency button feature that easily initiates an Emergency call to the police, a surveillance camera that streams live footage of the area, and an LCD screen that displays to the public awareness messages about the COVID-19 pandemic. © 2021 IEEE.