Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,s...Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efficient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method tofirst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to thefield of underwater archaeology,stock assessment,and surveillance.展开更多
Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management applications.In BODYNETs,rapid topology changes...Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management applications.In BODYNETs,rapid topology changes occur due to high node mobility,which affects the scalability of the network.Node clustering is one mechanism among many others,which is used to overcome this issue in BODYNETs.There are many clustering algorithms used in this domain to overcome this issue.However,these algorithms generate a large number of Cluster Heads(CHs),which results in scarce resource utilization and degraded performance.In this research,an efficient clustering technique is proposed to handle these problems.The transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational requirements.By optimizing the transmission range,the packet loss ratio is minimized,and link quality is improved,which leads to reduced energy consumption.To select optimal CHs the Whale Optimization Algorithm(WOA)is used based on their fitness,which enhances the network performance by reducing routing overhead.Our proposed scheme outclasses the existing state-of-the-art techniques,e.g.,Ant Colony Optimization(ACO),Gray Wolf Optimization(GWO),and Dragonfly Optimization Algorithm(DFA)in terms of energy consumption and cluster building time.展开更多
A wireless body area network(WBAN)consists of tiny healthmonitoring sensors implanted in or placed on the human body.These sensors are used to collect and communicate human medical and physiological data and represent...A wireless body area network(WBAN)consists of tiny healthmonitoring sensors implanted in or placed on the human body.These sensors are used to collect and communicate human medical and physiological data and represent a subset of the Internet of Things(IoT)systems.WBANs are connected to medical servers that monitor patients’health.This type of network can protect critical patients’lives due to the ability to monitor patients’health continuously and remotely.The inter-WBAN communication provides a dynamic environment for patients allowing them to move freely.However,during patient movement,the WBAN patient nodes may become out of range of a remote base station.Hence,to handle this problem,an efficient method for inter-WBAN communication is needed.In this study,a method using a cluster-based routing technique is proposed.In the proposed method,a cluster head(CH)acts as a gateway between the cluster members and the external network,which helps to reduce the network’s overhead.In clustering,the cluster’s lifetime is a vital parameter for network efficiency.Thus,to optimize the CH’s selection process,three evolutionary algorithms are employed,namely,the ant colony optimization(ACO),multi-objective particle swarm optimization(MOPSO),and the comprehensive learning particle swarm optimization(CLPSO).The performance of the proposed method is verified by extensive experiments by varying values of different parameters,including the transmission range,node number,node mobility,and grid size.A comprehensive comparative analysis of the three algorithms is conducted by extensive experiments.The results show that,compared with the other methods,the proposed ACO-based method can form clusters more efficiently and increase network lifetime,thus achieving remarkable network and energy efficiency.The proposed ACO-based technique can also be used in other types of ad-hoc networks as well.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2020R1G1A1099559).
文摘Currently,worldwide industries and communities are concerned with building,expanding,and exploring the assets and resources found in the oceans and seas.More precisely,to analyze a stock,archaeology,and surveillance,sev-eral cameras are installed underseas to collect videos.However,on the other hand,these large size videos require a lot of time and memory for their processing to extract relevant information.Hence,to automate this manual procedure of video assessment,an accurate and efficient automated system is a greater necessity.From this perspective,we intend to present a complete framework solution for the task of video summarization and object detection in underwater videos.We employed a perceived motion energy(PME)method tofirst extract the keyframes followed by an object detection model approach namely YoloV3 to perform object detection in underwater videos.The issues of blurriness and low contrast in underwater images are also taken into account in the presented approach by applying the image enhancement method.Furthermore,the suggested framework of underwater video summarization and object detection has been evaluated on a publicly available brackish dataset.It is observed that the proposed framework shows good performance and hence ultimately assists several marine researchers or scientists related to thefield of underwater archaeology,stock assessment,and surveillance.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)(No.NRF-2018R1C1B5038818).
文摘Body Area Networks(BODYNETs)or Wireless Body Area Networks(WBAN),being an important type of ad-hoc network,plays a vital role in multimedia,safety,and traffic management applications.In BODYNETs,rapid topology changes occur due to high node mobility,which affects the scalability of the network.Node clustering is one mechanism among many others,which is used to overcome this issue in BODYNETs.There are many clustering algorithms used in this domain to overcome this issue.However,these algorithms generate a large number of Cluster Heads(CHs),which results in scarce resource utilization and degraded performance.In this research,an efficient clustering technique is proposed to handle these problems.The transmission range of BODYNET nodes is dynamically tuned accordingly as per their operational requirements.By optimizing the transmission range,the packet loss ratio is minimized,and link quality is improved,which leads to reduced energy consumption.To select optimal CHs the Whale Optimization Algorithm(WOA)is used based on their fitness,which enhances the network performance by reducing routing overhead.Our proposed scheme outclasses the existing state-of-the-art techniques,e.g.,Ant Colony Optimization(ACO),Gray Wolf Optimization(GWO),and Dragonfly Optimization Algorithm(DFA)in terms of energy consumption and cluster building time.
基金supported by the National Research Foundation of Korea(NRF)funded by the Korea government(MSIT)(No.NRF-2018R1C1B5038818).
文摘A wireless body area network(WBAN)consists of tiny healthmonitoring sensors implanted in or placed on the human body.These sensors are used to collect and communicate human medical and physiological data and represent a subset of the Internet of Things(IoT)systems.WBANs are connected to medical servers that monitor patients’health.This type of network can protect critical patients’lives due to the ability to monitor patients’health continuously and remotely.The inter-WBAN communication provides a dynamic environment for patients allowing them to move freely.However,during patient movement,the WBAN patient nodes may become out of range of a remote base station.Hence,to handle this problem,an efficient method for inter-WBAN communication is needed.In this study,a method using a cluster-based routing technique is proposed.In the proposed method,a cluster head(CH)acts as a gateway between the cluster members and the external network,which helps to reduce the network’s overhead.In clustering,the cluster’s lifetime is a vital parameter for network efficiency.Thus,to optimize the CH’s selection process,three evolutionary algorithms are employed,namely,the ant colony optimization(ACO),multi-objective particle swarm optimization(MOPSO),and the comprehensive learning particle swarm optimization(CLPSO).The performance of the proposed method is verified by extensive experiments by varying values of different parameters,including the transmission range,node number,node mobility,and grid size.A comprehensive comparative analysis of the three algorithms is conducted by extensive experiments.The results show that,compared with the other methods,the proposed ACO-based method can form clusters more efficiently and increase network lifetime,thus achieving remarkable network and energy efficiency.The proposed ACO-based technique can also be used in other types of ad-hoc networks as well.
