Design of LMS algorithm for noise canceller based on FPGA

This paper presents the design of an adapting filtering method to remove the noise in the biomedical signal records. The major concern about analyze the presence of various artifacts in ECG records and modular artifacts in EEG records caused due to various noise factors. Here, we have proposed a design based on LMS (Least Mean Square) algorithm to remove the artifacts from biomedical signal using Verilog HDL based on been mapped on  commercially available FPGAs (Field Programmable Gate Arrays). In this design the LMS algorithm used as a noise canceller and the reference signal was adaptively filtered and subtracted from primary signal to obtain the estimated biomedical signal. The original biomedical signal can be reconstructed by passing the digital bit stream through a low pass filter. This design is suitable for its low power biomedical instrument design and it reduces the whole system cost. Keywords: LMS algorithm, noise canceller, Verilog HDL, artifacts, biomedical signal, Low power application

Aiming to Minimize Alcohol-Impaired Road Fatalities: Utilizing Fairness-Aware and Domain Knowledge-Infused Artificial Intelligence

Approximately 30% of all traffic fatalities in the United States are attributed to alcohol-impaired driving. This means that, despite stringent laws against this offense in every state, the frequency of drunk driving accidents is alarming, resulting in approximately one person being killed every 45 minutes. The process of charging individuals with Driving Under the Influence (DUI) is intricate and can sometimes be subjective, involving multiple stages such as observing the vehicle in motion, interacting with the driver, and conducting Standardized Field Sobriety Tests (SFSTs). Biases have been observed through racial profiling, leading to some groups and geographical areas facing fewer DUI tests, resulting in many actual DUI incidents going undetected, ultimately leading to a higher number of fatalities. To tackle this issue, our research introduces an Artificial Intelligence-based predictor that is both fairness-aware and incorporates domain knowledge to analyze DUI-related fatalities in different geographic locations. Through this model, we gain intriguing insights into the interplay between various demographic groups, including age, race, and income. By utilizing the provided information to allocate policing resources in a more equitable and efficient manner, there is potential to reduce DUI-related fatalities and have a significant impact on road safety.Comment: IEEE Big Data 202

Analysis of Pain Hemodynamic Response Using Near-Infrared Spectroscopy (NIRS)

Despite recent advances in brain research, understanding the various signals for pain and pain intensities in the brain cortex is still a complex task due to temporal and spatial variations of brain hemodynamics. In this paper we have investigated pain based on cerebral hemodynamics via near-infrared spectroscopy (NIRS). This study presents a pain stimulation experiment that uses three acupuncture manipulation techniques to safely induce pain in healthy subjects. Acupuncture pain response was presented and hemodynamic pain signal analysis showed the presence of dominant channels and their relationship among surrounding channels, which contribute the further pain research area.Comment: 11 pages, 11 figure

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Develop blood oxygen level dependent signal by metabolic/hemodynamic model using numerical methods

Background and objective: The metabolic/hemodynamic (MH) model describes the blood flow mechanisms as well as the coupling between the hemodynamic responses and the metabolic activities in a blood vessel in the human brain. In the existing MH model, the blood flow out from a blood vessel is formulated as dependent only on the capillary volume. In fact, the blood flow out from a blood vessel depends not only on the capillary volume but also on the blood flow into the capillary bed. For this reason, the blood flow out formula of the existing model has been modified. In addition to implementing existing model modification to obtain better accuracy, we have used new methods to solve the model instead of conventional methods. Method: The MH model describes physical phenomena of a blood vessel by eight processes equations (PEs). These PEs are often solved by using a local linearization (LL) scheme and the Taylor series method. In addition to the previously used Taylor series method, we have also used the Euler method and the Runge–Kutta (RK) method to solve the model instead of a LL scheme for estimating dynamical variables (DVs). By using these DVs, a Blood Oxygen Level Dependent (BOLD) signal is generated through a well-defined observation equation (OE). There are two OEs, called Obata and Friston. The Friston OE produces a BOLD signal from the cerebral blood volume and deoxy-hemoglobin content with their nonlinear properties; conversely, the Obata OE produces a BOLD signal without considering nonlinear properties. For this reason, we have used the Friston OE instead of the Obata OE to estimate the BOLD signal perfectly. Results: At 20% resting oxygen extraction fraction (ROEF), the BOLD signals of the modified and the existing model are identical, but when the ROEF increased up to 50% at its standard value, the modified model accuracy is increased by 16.12%–23.07% more than that of the existing model. The Euler and RK methods generate a BOLD signal 6.95% more accurately than that of Taylor series method from the modified model. Conclusion: In the model inversion process, this research will be helpful to estimate the model parameters and hidden states accurately

Analysis of the Oceanic Wave Dynamics for Generation of Electrical Energy Using a Linear Generator

Electricity generation from oceanic wave depends on the wave dynamics and the behavior of the ocean. In this paper, a permanent magnet linear generator (PMLG) has been designed and analyzed for oceanic wave energy conversion. The proposed PMLG design is suitable for the point absorber type wave energy device. A mathematical model of ocean wave is presented to observe the output characteristics and performance of the PMLG with the variation of ocean waves. The generated voltage, current, power, applied force, magnetic flux linkage, and force components of the proposed PMLG have been presented for different sea wave conditions. The commercially available software package ANSYS/ANSOFT has been used to simulate the proposed PMLG by the finite element method. The magnetic flux lines, flux density, and field intensity of the proposed PMLG that greatly varies with time are presented for transient analysis. The simulation result shows the excellent features of the PMLG for constant and variable speeds related to wave conditions. These analyses help to select proper PMLG parameters for better utilization of sea wave to maximize output power