Photoplethysmography and its application in clinical physiological measurement

In the single authored, expert voice that is this book's unique strength, Arnold Katz provides a comprehensive overview of the physiological and biophysical basis of cardiac function, beginning with structure and proceeding to biochemistry, biophysics, and pathophysiology in arrhythmias, ischemia, and heart failure. Emphasis is on the interrelationships of basic processes among the cell, cardiac muscle function, and the biophysics of contractile and electrical behavior. This edition includes new material on cell signaling and molecular biology.

Physiology of the heart

Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

Thank you for downloading bioelectrical signal processing in cardiac and neurological applications. Maybe you have knowledge that, people have search hundreds times for their chosen books like this bioelectrical signal processing in cardiac and neurological applications, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their computer.

Bioelectrical Signal Processing In Cardiac And Neurological Applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments' proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures.

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Electronic Noses for Environmental Monitoring Applications

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods.

/pdf/estimating-the-standardized-precipitation-evapotranspiration-2joegys1.pdf

Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh

Calibration of RGB Sensor for Estimation of Real-Time Correlated Colour Temperature using Machine Learning Regression Techniques

Abstract Countries depending on small-scale agriculture, such as Bangladesh, are susceptible to climate change and variability. Changes in the frequency and intensity of drought are a crucial aspect of this issue and the focus of this research. The goal of this work is to use SPI (standardized precipitation index) and SPEI (standardized precipitation evapotranspiration index) to investigate the differences in drought characteristics across different physiognomy types in Bangladesh and to highlight how drought characteristics change over time and spatial scales when considering different geomorphologies. This study used monthly precipitation and temperature data from 29 metrological stations for 39 years (1980–2018) for calculating SPI and SPEI values. To determine the significance of drought characteristic trends over different temporal and spatial scales, the modified Mann–Kendall trend test and multivariable linear regression (MLR) techniques were used. The results are as follows: (1) Overall, decreasing dry trend was found in Eastern hill regions, whereas an increasing drought trends were found in the in the rest of the regions in all time scaless (range is from − 0.08 decade −1 to − 0.15 decade −1 for 3-month time scale). However, except for the one-month time scale, the statistically significant trend was identified mostly in the north-central and northeast regions, indicating that drought patterns migrate from the northwest to the center region. (2) SPEI is anticipated to be better at capturing dry/wet cycles in more complex regions than SPI. (3) According to the MLR, longitude and maximum temperature can both influence precipitation. (4) Drought intensity increased gradually from the southern to the northern regions (1.26–1.56), and drought events occurred predominantly in the northwestern regions (27–30 times), indicating that drought meteorological hotspots were primarily concentrated in the Barind Tract and Tista River basin over time. Findings can be used to improve drought evaluation, hazard management, and application policymaking in Bangladesh. This has implications for agricultural catastrophe prevention and mitigation. 

/pdf/spatiotemporal-drought-analysis-in-bangladesh-using-the-w4k6uesz.pdf

Spatiotemporal drought analysis in Bangladesh using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI)

Controlling concentration and temperature of continuously stirred tank reactor (CSTR) is an extremely challenging task in chemical process industries. This is because conventional unity feedback schemes do not guarantee stable operating conditions for the CSTR process. Therefore, fractional calculus is augmented with multi‐loop control to achieve enhanced stability and closed‐loop performance than unity feedback structure in this work. Accordingly, three different fractional‐order‐based novel multi‐loop control structures are proposed based on time domain analysis. In two of the proposed strategies, fractional‐order PID controller (FOPID) and internal model control (IMC)‐based FOPID controller with fractional filter are used in the inner‐loop. Moreover, fractional‐order‐based Lyapunov stability rule of model reference adaptive control (MRAC) is used in outer‐loops for both of the above mentioned methods. Finally, an advanced multi‐loop predictor with FOPD controller in the inner‐loop and FOIMC controller in the outer‐loop is also proposed. In this work, fractional‐order, filter parameters, and FOPID settings are obtained by minimizing multi‐objective functions using modified particle swarm optimization (PSO) algorithm. Closed‐loop responses and control efforts of the proposed control strategies are compared with that of FO‐Lyapunov‐based MRAC scheme. Quantitative performance analysis is also carried out on all proposed methods based on error metrics and total variation of the control signal.

Palash Kumar Kundu

Papers

Estimating the Standardized Precipitation Evapotranspiration Index Using Data-Driven Techniques: A Regional Study of Bangladesh

Calibration of RGB Sensor for Estimation of Real-Time Correlated Colour Temperature using Machine Learning Regression Techniques

Spatiotemporal drought analysis in Bangladesh using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI)

Improved fractional augmented control strategies for continuously stirred tank reactors

Machine Learning Based Illuminance Estimation from RGB Sensor in a Wireless Network