Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it...Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.展开更多
Mechanical signal capture without physical contact has emerged as a highly promising research field and attracted tremendous attention due to its prosperous applications in household medical care,lifestyle monitoring ...Mechanical signal capture without physical contact has emerged as a highly promising research field and attracted tremendous attention due to its prosperous applications in household medical care,lifestyle monitoring and remote operation,offering users high level of safety,convenience and comfort.Moreover,noncontact sensing is ideal to maximize the immersive user experience in the human–machine interaction(HMI),eliminating interference to human activities and mechanical fatigue to the sensor,simultaneously.Herein,we report a self-powered flexible sensor integrated with irradiation cross-linked polypropylene(IXPP)piezoelectret film for noncontact sensing,featuring multi-functions to detect mechanical signals transmitted through solid,liquid and gaseous media and would facilitate their versatile practical applications.The folded-structure configuration of the sensor facilitates the improvement of the noncontact sensing sensitivity.For solid media,such as the rectangular wooden stick used in this study,the sensor can detect mechanical stimulus exerted at a distance of 100 cm.A system detection sensitivity up to 57 pC/kPa with a low detection limit of 0.6 kPa is achieved at a noncontact distance of 10 cm.Even when partly or completely immersed in water,the sensor effectively traces movement signals of human bodies underwater,demonstrating great advantages for non-inductive aquatic fitness training monitoring.Furthermore,due to the low acoustic impedance of piezoelectret film,speech recognition through gaseous medium is also achieved.We further introduce application demonstrations of the developed film sensors to monitor exercise postures and physiological signals without direct contact between human body and the sensor,displaying great potential to be incorporated into future smart electronics.This study commendably expands the application scope of piezoelectret materials,which will have profound implications for exploring novel intelligent human–machine interactions.展开更多
Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extractio...Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre-and post-disaster RS images.However,RS images are usually limited to the revisit cycle and cloud cover.To solve this issue,social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster,which leads itself a compensation for RS images.In this paper,we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data,near real-time RS image and historical geographic information,in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability.Firstly,to generate a coarse waterlogging probability map,the historical inundated areas are derived from Digital Elevation Model(DEM)and historical waterlogging points,then the geographic features are extracted from DEM and RS image,which will be input to a Random Forest(RF)classifier to estimate the likelihood of hazards.Secondly,the real-time waterlogging-related information is extracted from social media data,where the Convolutional Neural Network(CNN)model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel.Finally,fine waterlogging probability map scan be generated based on morphological method,in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect.The 2016 Wuhan waterlogging and 2018 Chengdu water-logging are taken as case studies to demonstrate the effectiveness of the proposed framework.It can be conclud展开更多
Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple inpu...Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.展开更多
In the last decade or so the radiative transfer (RT) theory has been widely applied to the numerical simulations of scattering and thermal emission in both passive and active remote sensing. However, the model of scat...In the last decade or so the radiative transfer (RT) theory has been widely applied to the numerical simulations of scattering and thermal emission in both passive and active remote sensing. However, the model of scattering media has been largely restrictive to the horizontallyinfinite, stratified random media where the scatterers are homogeneously and randomly distributed. Correspondingly, the RT equation is one-dimensional (1-D). Recent展开更多
基金This work is supported by the National Natural Science Foundation of China[Grants Number 41771452,41771454 and 41890820]the Natural Science Fund of Hubei Province in China[Grant Number 2018CFA007].
文摘Urbanization is one of the most impactful human activities across the world today affecting the quality of urban life and its sustainable development.Urbanization in Africa is occurring at an unprecedented rate and it threatens the attainment of Sustainable Development Goals(SDGs).Urban sprawl has resulted in unsustainable urban development patterns from social,environmental,and economic perspectives.This study is among the first examples of research in Africa to combine remote sensing data with social media data to determine urban sprawl from 2011 to 2017 in Morogoro urban municipality,Tanzania.Random Forest(RF)method was applied to accomplish imagery classification and location-based social media(Twitter usage)data were obtained through a Twitter Application Programming Interface(API).Morogoro urban municipality was classified into built-up,vegetation,agriculture,and water land cover classes while the classification results were validated by the generation of 480 random points.Using the Kernel function,the study measured the location of Twitter users within a 1 km buffer from the center of the city.The results indicate that,expansion of the city(built-up land use),which is primarily driven by population expansion,has negative impacts on ecosystem services because pristine grasslands and forests which provide essential ecosystem services such as carbon sequestration and support for biodiversity have been replaced by built-up land cover.In addition,social media usage data suggest that there is the concentration of Twitter usage within the city center while Twitter usage declines away from the city center with significant spatial and numerical increase in Twitter usage in the study area.The outcome of the study suggests that the combination of remote sensing,social sensing,and population data were useful as a proxy/inference for interpreting urban sprawl and status of access to urban services and infrastructure in Morogoro,and Africa city where data for urban planning is often unavailable,inaccurate,or stale.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.62201392,12174061 and 12374451).
文摘Mechanical signal capture without physical contact has emerged as a highly promising research field and attracted tremendous attention due to its prosperous applications in household medical care,lifestyle monitoring and remote operation,offering users high level of safety,convenience and comfort.Moreover,noncontact sensing is ideal to maximize the immersive user experience in the human–machine interaction(HMI),eliminating interference to human activities and mechanical fatigue to the sensor,simultaneously.Herein,we report a self-powered flexible sensor integrated with irradiation cross-linked polypropylene(IXPP)piezoelectret film for noncontact sensing,featuring multi-functions to detect mechanical signals transmitted through solid,liquid and gaseous media and would facilitate their versatile practical applications.The folded-structure configuration of the sensor facilitates the improvement of the noncontact sensing sensitivity.For solid media,such as the rectangular wooden stick used in this study,the sensor can detect mechanical stimulus exerted at a distance of 100 cm.A system detection sensitivity up to 57 pC/kPa with a low detection limit of 0.6 kPa is achieved at a noncontact distance of 10 cm.Even when partly or completely immersed in water,the sensor effectively traces movement signals of human bodies underwater,demonstrating great advantages for non-inductive aquatic fitness training monitoring.Furthermore,due to the low acoustic impedance of piezoelectret film,speech recognition through gaseous medium is also achieved.We further introduce application demonstrations of the developed film sensors to monitor exercise postures and physiological signals without direct contact between human body and the sensor,displaying great potential to be incorporated into future smart electronics.This study commendably expands the application scope of piezoelectret materials,which will have profound implications for exploring novel intelligent human–machine interactions.
基金This project was supported by the China Postdoctoral Science Foundation[grant number 2017M622522].
文摘Urban waterlogging probability assessment is critical to emergency response and policymaking.Remote Sensing(RS)is a rich and reliable data source for waterlogging monitoring and evaluation through water body extraction derived from the pre-and post-disaster RS images.However,RS images are usually limited to the revisit cycle and cloud cover.To solve this issue,social media data have been considered as another data source which are immune to the weather such as clouds and can reflect the real-time public response for disaster,which leads itself a compensation for RS images.In this paper,we propose a coarse-to-fine waterlogging probability assessment framework based on multisource data including real-time social media data,near real-time RS image and historical geographic information,in which a coarse waterlogging probability map is refined by using the real-time information extracted from social media data to acquire a more accurate waterlogging probability.Firstly,to generate a coarse waterlogging probability map,the historical inundated areas are derived from Digital Elevation Model(DEM)and historical waterlogging points,then the geographic features are extracted from DEM and RS image,which will be input to a Random Forest(RF)classifier to estimate the likelihood of hazards.Secondly,the real-time waterlogging-related information is extracted from social media data,where the Convolutional Neural Network(CNN)model is applied to exploit the semantic information of sentences by capturing the local and position-invariant features using convolution kernel.Finally,fine waterlogging probability map scan be generated based on morphological method,in which real-time waterlogging-related social media data are taken as isolated highlight point and used to refine the coarse waterlogging probability map by a gray dilation pattern considering the distance-decay effect.The 2016 Wuhan waterlogging and 2018 Chengdu water-logging are taken as case studies to demonstrate the effectiveness of the proposed framework.It can be conclud
文摘Media based modulation(MBM)is expected to be a prominent modulation scheme,which has access to the high data rate by using radio frequency(RF)mirrors and fewer transmit antennas.Associated with multiuser multiple input multiple output(MIMO),the MBM scheme achieves better performance than other conventional multiuser MIMO schemes.In this paper,the massive MIMO uplink is considered and a conjunctive MBM transmission scheme for each user is employed.This conjunctive MBM transmission scheme gathers aggregate MBM signals in multiple continuous time slots,which exploits the structured sparsity of these aggregate MBM signals.Under this kind of scenario,a multiuser detector with low complexity based on the compressive sensing(CS)theory to gain better detection performance is proposed.This detector is developed from the greedy sparse recovery technique compressive sampling matching pursuit(CoSaMP)and exploits not only the inherently distributed sparsity of MBM signals but also the structured sparsity of multiple aggregate MBM signals.By exploiting these sparsity,the proposed CoSaMP based multiuser detector achieves reliable detection with low complexity.Simulation results demonstrate that the proposed CoSaMP based multiuser detector achieves better detection performance compared with the conventional methods.
基金Project supported by the National Natural Science Foundation of China, and Fok Ying Tung Education Foundation.
文摘In the last decade or so the radiative transfer (RT) theory has been widely applied to the numerical simulations of scattering and thermal emission in both passive and active remote sensing. However, the model of scattering media has been largely restrictive to the horizontallyinfinite, stratified random media where the scatterers are homogeneously and randomly distributed. Correspondingly, the RT equation is one-dimensional (1-D). Recent
