Multiradar Data Fusion for Respiratory Measurement of Multiple People

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system

Multiradar Data Fusion for Respiratory Measurement of Multiple People

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system

Radar-Based Estimation of Human Body Orientation Using Respiratory Features and Hierarchical Regression Model

This study proposes an accurate method to estimate human body orientation using a millimeter-wave radar system. Body displacement is measured from the phase of the radar echo, which is analyzed to obtain features associated with the fundamental and higher-order harmonic components of the quasi-periodic respiratory motion. These features are used in body-orientation estimation invoking a novel hierarchical regression model in which a logistic regression model is adopted in the first step to determine whether the target person is facing forwards or backwards; a pair of ridge regression models are employed in the second step to estimate body-orientation angle. To evaluate the performance of the proposed method, respiratory motions of five participants were recorded using three millimeter-wave radar systems; cross-validation was also performed. The average error in estimating body orientation angle was 38.3$^\circ$ and 23.1$^\circ$ using respectively a conventional method with only the fundamental frequency component and our proposed method, indicating an improvement in accuracy by factor 1.7 when using the proposed method. In addition, the coefficient of correlation between the actual and estimated body-orientation angles using the conventional and proposed methods are 0.74 and 0.91, respectively. These results show that by combining the characteristic features of the fundamental and higher-order harmonics from the respiratory motion, the proposed method offers better accuracy.Comment: 5 pages, 4 figures. This work is going to be submitted to the IEEE for possible publicatio

Radar-Based Estimation of Human Body Orientation Using Respiratory Features and Hierarchical Regression Model

This letter proposes an accurate method to estimate human body orientation using a millimeter-wave radar system. Body displacement is measured from the phase of the radar echo, which is analyzed to obtain features associated with the fundamental and higher order harmonic components of the quasi-periodic respiratory motion. These features are used in body orientation estimation invoking a novel hierarchical regression model in which a logistic regression model is adopted in the first step to determine whether the target person is facing forward or backward; a pair of ridge regression models is employed in the second step to estimate body orientation angle. To evaluate the performance of the proposed method, respiratory motions of five participants were recorded using three millimeter-wave radar systems; cross validation was also performed. The average error in estimating body orientation angle was 38.3 ∘ and 23.1 ∘ using, respectively, a conventional method with only the fundamental frequency component and our proposed method, indicating an improvement in accuracy by a factor of 1.7 when using the proposed method. In addition, the coefficients of correlation between the actual and estimated body orientation angles using the conventional and proposed methods are 0.74 and 0.91, respectively. These results show that by combining the characteristic features of the fundamental and higher order harmonics from the respiratory motion, the proposed method offers better accuracy

Can Land Use Regulations and Taxes Help Mitigate Vehicular CO2 emissions?: An Empirical Study of Japanese Cities

This study advocates a multi-dimensional urban planning strategy to help combat climate change under local—and not national—policies. However, the literature does not provide adequate guidance to local governments seeking to enhance urbanization and in turn reduce vehicular carbon dioxide (CO2) emissions. Therefore, this study sheds light on the effects of the following four urban planning instruments on vehicular CO2 emissions: urbanization promoting areas, urbanization control areas, urban planning taxes and property taxes. Using Japanese city-level data from 1990 to 2010, we find that the two urbanization area planning instruments and the urban planning taxes help lower emissions by increasing population density in low-density cities and that property taxes help reduce emissions in high-density cities. However, the increased population density associated with these instruments can lead to other negative outcomes, including increased traffic accidents, increased crime and a decrease in the facility condition index. City governments should consider complementary policies to mitigate such negative outcomes when employing planning instruments aiming to increase population density

How do urban characteristics affect climate change mitigation policies?

Urban policy plays an important role in urbanization and urban sprawl, which in turn affect changes in CO2 emissions from urban areas. However, urban planning policies that consider climate change mitigation have not been widely adopted, despite the issue\u27s importance. To promote the consideration of climate change policies in urban planning, it is useful to identify the key determinants of effective climate change mitigation for policy makers. The objective of this study is therefore to identify the determining factors that affect changes in urban CO2 emissions based on city type using a dataset of metropolitan areas. We obtained data on 276 cities in 26 countries for the years 2000, 2005, and 2008. We divided the data into five regional groups and four clusters to control for the characteristics of metropolitan areas. The dataset includes urban CO2 emissions, GDP, and population. Three variables related to urban characteristics are subjected to determinants analysis using an econometric approach. The results show that the determinants of changes in urban CO2 emissions differ by city type and region. These results contribute to a better understanding of urban policies that can improve the effects of these driving factors by considering the characteristics of each city type

Corporate environmental and economic performances of Japanese manufacturing firms: Empirical study for sustainable development

This study examines the relationships between environmental performance and economic performance in Japanese manufacturing firms. The environmental performance indicators include CO2 emissions and the aggregate toxic risk associated with chemical emissions relative to sales. Return on assets (ROA) is used as an indicator of economic performance. We demonstrate that there is a significant, inverted U-shaped relationship between ROA and environmental performance calculated by aggregated toxic risk. We also find that the environmental performance increases ROA through both returns on sales and capital turnover improvement. However, we observe a significant, positive relationship between financial performance and environmental performance based on CO2 emissions. These findings may provide evidence for the consequences of environmental firm behavior and sustainable development

An Analysis of Urban Environmental Kuznets Curve of CO2 Emissions: Empirical Analysis of 276 Global Metropolitan Areas

This study analyzed the relationship between urban CO2 emissions and economic growth applying the environmental Kuznets curve hypothesis. The objective of this study is to investigate how urban CO2 emissions and their composition have changed with urban economic growth, depending on city characteristics, using a dataset of metropolitan areas. We obtained data for 276 cities in 26 countries for the years 2000, 2005, and 2008. The dataset includes urban CO2 emissions, GDP, and population. Additionally, data regarding compact city variables are applied to determinants analysis using an econometric approach. The results demonstrate an inverted U-shape relationship between urban CO2 emissions and urban economic growth. Additionally, an inverted U-shape relationship is observed for the transport and residential & industry sectors. However, the turning points of each inverted U-shape curve varies. This result implies that we can better understand urban policies for reducing urban CO2 emissions by considering the characteristics of each sector

Predicting the Biological Effects of Human Salivary Gland Tumour Cells for Scanned 4He-, 12C-, 16O-, and 20Ne-Ion Beams Using an SOI Microdosimeter

Experimental microdosimetry along with the microdosimetric kinetic (MK) model can be utilized to predict the biological effects of ions. To predict the relative biological effectiveness (RBE) of ions and the survival fraction (SF) of human salivary gland tumour (HSGc-C5) cells, microdosimetric quantities measured by a silicon-on-insulator (SOI) MicroPlus-mushroom microdosimeter along the spread-out Bragg peak (SOBP) delivered by pencil beam scanning of 4He, 12C, 16O, and 20Ne ions were used. The MK model parameters of HSGc-C5 cells were obtained from the best fit of the calculated SF for the different linear energy transfer (LET) of these ions and the formerly reported in vitro SF for the same LET and ions used for calculations. For a cube-shaped target of 10 × 10 × 6 cm3, treatment plans for 4He, 12C, 16O, and 20Ne ions were produced with proprietary treatment planning software (TPS) aiming for 10% SF of HSGc-C5 cells over the target volume and were delivered to a polymethyl methacrylate (PMMA) phantom. Afterwards, the saturation-corrected dose-mean lineal energy derived based on the measured microdosimetry spectra, along with the physical dose at various depths in PMMA phantoms, was used for the estimation of the SF, RBE, and RBE-weighted dose using the MK model. The predicted SF, RBE, and the RBE-weighted dose agreed with what was planned by the TPS within 3% at most depths for these ions.publishedVersio