As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract ke...As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).展开更多
The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are co...The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.展开更多
Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumpti...Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.展开更多
Visual image sensor is developed to detect the weld pool images in pulsed MIG welding. An exposure controller, which is composed of the modules of the voltage transforming, the exposure parameters presetting, the comp...Visual image sensor is developed to detect the weld pool images in pulsed MIG welding. An exposure controller, which is composed of the modules of the voltage transforming, the exposure parameters presetting, the complex programmable logic device （CPLD） based logic controlling, exposure signal processing, the arc state detecting, the mechanical iris driving and so on, is designed at first. Then, a visual image sensor consists of an ordinary CCD camera, optical system and exposure controller is established. The exposure synchronic control logic is described with very-high-speed integrated circuit hardware description language （VHDL） and programmed with CPLD , to detect weld pool images at the stage of base current in pulsed MIG welding. Finally, both bead on plate welding and V groove filled welding are carried out, clear and consistent weld pool images are acquired.展开更多
Dual-atom catalysts have recently been recognized as promising alternatives to natural metalloenzymes.Inspired by the complex polymetallic active ce nters of natural metalloenzymes,we demonstrate the feasibility of th...Dual-atom catalysts have recently been recognized as promising alternatives to natural metalloenzymes.Inspired by the complex polymetallic active ce nters of natural metalloenzymes,we demonstrate the feasibility of the nitrogen-bridge d diatomic iron catalysts(Fe_(2)-N_(6)-C)as highly efficient dual-e nzyme mimics.It is noted that the as-prepared Fe_(2)-N_(6)-C catalysts show much higher activity than the single-atom iron catalysts(Fe_(1)-N_(4)-C).Experimental characterizations and theoretical simulations reveal that the N-bridged FeFe sites with an appropriate distance exert a synergy to enable more optimal intermediates adsorption and desorption,thus realizing enhanced dual-enzymatic performance.Taking advantage of the intrinsic dual-enzyme mimic activity of Fe_(2)-N_(6)-C catalysts,an innovative two-channel visual sensor array is constructed to distinguish and detect five common antioxidants.The sensor array can successfully identify 20 blind samples with a success rate of 100%.Moreover,a portable smartphone-based visual sensor assay is proposed,which could achieve on-site detection and differentiation of antioxidants based on their distinct color change.The present study illustrates the great potential of dual-atom catalyst-based sensor arrays as a promising visual platform for sensing applications.展开更多
基金Dr.Deepak Dahiya would like to thank Deanship of Scientific Research at Majmaah University for supporting his work under Project No.(R-2022-96)。
文摘As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).
基金supported by National Natural Science Foundation of China (Nos. 60805038 and 60725309)Beijing Natural Science Foundation (No. 4082032)
文摘The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.
基金This paper was supported partially by the Natural Science Foundation of China under Grants No. 60833009, No. 61003280 the National Science Fund for Distinguished Young Scholars under Grant No. 60925010+1 种基金 the Funds for Creative Research Groups of China under Grant No.61121001 the Pro- gram for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049.
文摘Pedestrian detection is one of the most important problems in the visual sensor network. Considering that the visual sensors have limited cap ability, we propose a pedestrian detection method with low energy consumption. Our method contains two parts: one is an Enhanced Self-Organizing Background Subtraction (ESOBS) based foreground segmentation module to obtain active areas in the observed region from the visual sensors; the other is an appearance model based detection module to detect the pedestrians from the foreground areas. Moreover, we create our own large pedestrian dataset according to the specific scene in the visual sensor network. Numerous experiments are conducted in both indoor and outdoor specific scenes. The experimental results show that our method is effective.
基金This work was supported by the National High Technology Research and Development Program("863"Program) of China ( ContractNo 2007AA04Z258)
文摘Visual image sensor is developed to detect the weld pool images in pulsed MIG welding. An exposure controller, which is composed of the modules of the voltage transforming, the exposure parameters presetting, the complex programmable logic device （CPLD） based logic controlling, exposure signal processing, the arc state detecting, the mechanical iris driving and so on, is designed at first. Then, a visual image sensor consists of an ordinary CCD camera, optical system and exposure controller is established. The exposure synchronic control logic is described with very-high-speed integrated circuit hardware description language （VHDL） and programmed with CPLD , to detect weld pool images at the stage of base current in pulsed MIG welding. Finally, both bead on plate welding and V groove filled welding are carried out, clear and consistent weld pool images are acquired.
基金supported by the National Natural Science Foundation of China(22172063)the Independent Cultivation Program of Innovation Team of Ji’nan City(2021GXRC052)the Young Taishan Scholar Program(tsqn201812080)。
文摘Dual-atom catalysts have recently been recognized as promising alternatives to natural metalloenzymes.Inspired by the complex polymetallic active ce nters of natural metalloenzymes,we demonstrate the feasibility of the nitrogen-bridge d diatomic iron catalysts(Fe_(2)-N_(6)-C)as highly efficient dual-e nzyme mimics.It is noted that the as-prepared Fe_(2)-N_(6)-C catalysts show much higher activity than the single-atom iron catalysts(Fe_(1)-N_(4)-C).Experimental characterizations and theoretical simulations reveal that the N-bridged FeFe sites with an appropriate distance exert a synergy to enable more optimal intermediates adsorption and desorption,thus realizing enhanced dual-enzymatic performance.Taking advantage of the intrinsic dual-enzyme mimic activity of Fe_(2)-N_(6)-C catalysts,an innovative two-channel visual sensor array is constructed to distinguish and detect five common antioxidants.The sensor array can successfully identify 20 blind samples with a success rate of 100%.Moreover,a portable smartphone-based visual sensor assay is proposed,which could achieve on-site detection and differentiation of antioxidants based on their distinct color change.The present study illustrates the great potential of dual-atom catalyst-based sensor arrays as a promising visual platform for sensing applications.
