SCATTERING EFFECTS BY SiO2 NANO-MICROPARTICLE AND FREE SPACE ATTENUATION MODELLING FOR DIVERSE WEATHER CONDITIONS

This article presents few empirical models to be used prediction of atmospheric attenuation due to airborne suspended particles such as sand, dust, fog and mist. The attenuation can be determined using the proposed models as standard methods considering various types of weather conditions including silicadominant sandstorm, dust storm, fog or mist. As atmospheric attenuation heavily affects the transmission of data using visible light communication, we study the effect of air suspended particles to scattering amplitude and turbulence phase of the light beam. The proposed models are compared with existing attenuation models in the case of attenuation under foggy weather condition. The proposed models especially proposed Model 2 is seen to be best fit for prediction of atmospheric attenuation under dense to very light fog weather

Estimating Blood Pressure from Photoplethysmogram Signal and Demographic Features using Machine Learning Techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the Blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous and a non-invasive BP measurement system is proposed using Photoplethysmogram (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo pre-processing and feature extraction steps. Time, frequency and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for Systolic BP (SBP) and Diastolic BP (DBP) estimation individually. Gaussian Process Regression (GPR) along with ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root-mean-square error (RMSE) of 6.74 and 3.59 respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.Comment: Accepted for publication in Sensor, 14 Figures, 14 Table

A systematic approach to the design and characterization of a smart insole for detecting vertical ground reaction force (vGRF) in gait analysis

Gait analysis is a systematic study of human locomotion, which can be utilized in various applications, such as rehabilitation, clinical diagnostics and sports activities. The various limitations such as cost, non-portability, long setup time, post-processing time etc., of the current gait analysis techniques have made them unfeasible for individual use. This led to an increase in research interest in developing smart insoles where wearable sensors can be employed to detect vertical ground reaction forces (vGRF) and other gait variables. Smart insoles are flexible, portable and comfortable for gait analysis, and can monitor plantar pressure frequently through embedded sensors that convert the applied pressure to an electrical signal that can be displayed and analyzed further. Several research teams are still working to improve the insoles' features such as size, sensitivity of insoles sensors, durability, and the intelligence of insoles to monitor and control subjects' gait by detecting various complications providing recommendation to enhance walking performance. Even though systematic sensor calibration approaches have been followed by different teams to calibrate insoles' sensor, expensive calibration devices were used for calibration such as universal testing machines or infrared motion capture cameras equipped in motion analysis labs. This paper provides a systematic design and characterization procedure for three different pressure sensors: force-sensitive resistors (FSRs), ceramic piezoelectric sensors, and flexible piezoelectric sensors that can be used for detecting vGRF using a smart insole. A simple calibration method based on a load cell is presented as an alternative to the expensive calibration techniques. In addition, to evaluate the performance of the different sensors as a component for the smart insole, the acquired vGRF from different insoles were used to compare them. The results showed that the FSR is the most effective sensor among the three sensors for smart insole applications, whereas the piezoelectric sensors can be utilized in detecting the start and end of the gait cycle. This study will be useful for any research group in replicating the design of a customized smart insole for gait analysis. 2020 by the authors. Licensee MDPI, Basel, Switzerland.This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library. The authors would like to thank Engr. Ayman Ammar, Electrical Engineering, Qatar University for helping in printing the printed circuit boards (PCBs). This research was partially funded by Qatar National Research Foundation (QNRF), grant number NPRP12S-0227-190164 and Research University Grant DIP-2018-017. The publication of this article was funded by the Qatar National Library.Scopu

Advances on Low Power Designs for SRAM Cell

As the development of complex metal oxide semiconductor (CMOS) technology, fast low-power static random access memory (SRAM) has become an important component of many very large scale integration (VLSI) chips. Lot of applications preferred to use the 6T SRAM because of its robustness and very high speed. However, the leakage current has increasing with the increase SRAM size. It consumes more power while in standby condition. The power dissipation has become an importance consideration due to the increase integration, operating speeds and the explosive growth of battery operated appliances. The objective of this paper is to review and discuss several methods to overcome the power dissipation problem of SRAM. Low power SRAM can be produced with improvement in term of power dissipation during the standby condition, write operation and read operation. Discharging and charging of bit lines consumes more power during write ‘0’ and ‘1’compared to read operation. One of the methods to produce low power SRAM design is with make modification circuit at a standard 6T SRAM cell. This modification circuit will help to decrease power dissipation and leakage current. Several method was discussed in this paper for understand the method to produce low power design of SRAM cell. Recommendations for future research are also set out. This review gives some idea for future research to improve the design of low power SRAM cell

A 5Gbit/s CMOS clock and data recovery circuit

This paper presents a half-rate 5Gb/s clock and data recovery circuit. Retiming of data is done by the linear PD that provides practically no systematic offset for the frequency band of interest The circuit was designed in a 0.18-mu m CMOS process and occupies an active area of 0.2 x 0.32 mm(2). The CDR exhibits an RMS jitter of +/- 1.2 ps and a peak-to-peak jitter of 5ps. The power dissipation is 97mW from a 1.8-V supply

Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes. 2020 by the authors. Licensee MDPI, Basel, Switzerland.Funding: This work was made possible by NPRP12S-0227-190164 from the Qatar National Research Fund, a member of Qatar Foundation, Doha, Qatar. The statements made herein are solely the responsibility of the authors.Scopu

Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients. 2022 by the authors.This work was supported by the Faculty of Medicine, Universiti Kebangsaan Malaysia (UKM), and UKM Grant Number DIP-2020-004, Grant Number GP-2020-K017701, and by the Qatar National Research fund under Grant UREP28-144-3-046. The statements made herein are solely the responsibility of the authors.Scopu