Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traf...Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.展开更多
Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s...Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.展开更多
Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievem...Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievements in agricultural robotics,specifically those that are used for autonomous weed control,field scouting,and harvesting.Object identification,task planning algorithms,digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming.The concepts of multi-robots,human-robot collaboration,and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming.It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.For the case of robotic harvesting,an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators.While robots are becoming the inseparable parts of the modern farms,our conclusion is that it is not realistic to expect an entirely automated farming system in the future.展开更多
Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture(CEA)facilities that projected the image of plant factories for urban agricult...Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture(CEA)facilities that projected the image of plant factories for urban agriculture.The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings.Successful deployment of CEA for urban agriculture requires many components and subsystems,as well as the understanding of the external influencing factors that should be systematically considered and integrated.This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation,which is necessary for a successful transition to urban agriculture.This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials,environment perception and data sharing,and advanced microclimate control and energy optimization models.This research highlighted urban agriculture and its derivatives,including vertical farming,rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population,environmental degradation,and urbanization that are threatening food security.Finally,several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture.展开更多
Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplina...Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments.With the exception of milking robots,the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date.To accelerate this pace,simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios.This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications.The key features and performance efficiency of three selected simulators were also compared.A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform.Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard.It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment.This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces,algorithms,and reusing the materials.展开更多
基金National Key R&D Program of China,Grant/Award Number:2022YFC3303600National Natural Science Foundation of China,Grant/Award Number:62077015Natural Science Foundation of Zhejiang Province,Grant/Award Number:LY23F020010。
文摘Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.
文摘Disease diagnosis is a challenging task due to a large number of associated factors.Uncertainty in the diagnosis process arises frominaccuracy in patient attributes,missing data,and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables.This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things(IoT)empowered by the fuzzy inference system(FIS)to diagnose various diseases.The Fuzzy Systemis one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties,and fuzzy logic is the best way to handle uncertainties.Our proposed system differentiates new cases provided symptoms of the disease.Generally,it becomes a time-sensitive task to discriminate symptomatic diseases.The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently.Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease.This study aims to differentiate and diagnose COVID-19,Typhoid,Malaria,and Pneumonia.This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms.MATLAB tool is utilised for the implementation of FIS.Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms.The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases.This study may assist doctors,patients,medical practitioners,and other healthcare professionals in early diagnosis and better treat diseases.
文摘Digital farming is the practice of modern technologies such as sensors,robotics,and data analysis for shifting from tedious operations to continuously automated processes.This paper reviews some of the latest achievements in agricultural robotics,specifically those that are used for autonomous weed control,field scouting,and harvesting.Object identification,task planning algorithms,digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming.The concepts of multi-robots,human-robot collaboration,and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming.It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information.For the case of robotic harvesting,an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators.While robots are becoming the inseparable parts of the modern farms,our conclusion is that it is not realistic to expect an entirely automated farming system in the future.
文摘Greenhouse cultivation has evolved from simple covered rows of open-fields crops to highly sophisticated controlled environment agriculture(CEA)facilities that projected the image of plant factories for urban agriculture.The advances and improvements in CEA have promoted the scientific solutions for the efficient production of plants in populated cities and multi-story buildings.Successful deployment of CEA for urban agriculture requires many components and subsystems,as well as the understanding of the external influencing factors that should be systematically considered and integrated.This review is an attempt to highlight some of the most recent advances in greenhouse technology and CEA in order to raise the awareness for technology transfer and adaptation,which is necessary for a successful transition to urban agriculture.This study reviewed several aspects of a high-tech CEA system including improvements in the frame and covering materials,environment perception and data sharing,and advanced microclimate control and energy optimization models.This research highlighted urban agriculture and its derivatives,including vertical farming,rooftop greenhouses and plant factories which are the extensions of CEA and have emerged as a response to the growing population,environmental degradation,and urbanization that are threatening food security.Finally,several opportunities and challenges have been identified in implementing the integrated CEA and vertical farming for urban agriculture.
文摘Research efforts for development of agricultural robots that can effectively perform tedious field tasks have grown significantly in the past decade.Agricultural robots are complex systems that require interdisciplinary collaborations between different research groups for effective task delivery in unstructured crops and plants environments.With the exception of milking robots,the extensive research works that have been carried out in the past two decades for adaptation of robotics in agriculture have not yielded a commercial product to date.To accelerate this pace,simulation approach and evaluation methods in virtual environments can provide an affordable and reliable framework for experimenting with different sensing and acting mechanisms in order to verify the performance functionality of the robot in dynamic scenarios.This paper reviews several professional simulators and custom-built virtual environments that have been used for agricultural robotic applications.The key features and performance efficiency of three selected simulators were also compared.A simulation case study was demonstrated to highlight some of the powerful functionalities of the Virtual Robot Experimentation Platform.Details of the objects and scenes were presented as the proof-of-concept for using a completely simulated robotic platform and sensing systems in a virtual citrus orchard.It was shown that the simulated workspace can provide a configurable and modular prototype robotic system that is capable of adapting to several field conditions and tasks through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment.This review suggests that an open-source software platform for agricultural robotics will significantly accelerate effective collaborations between different research groups for sharing existing workspaces,algorithms,and reusing the materials.
