Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are s...Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.展开更多
In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge ...In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.展开更多
Liberalized electricity markets,smart grids and high penetration of renewable energy sources(RESs)led to the development of novel markets,whose objective is the harmonization between production and demand,usually note...Liberalized electricity markets,smart grids and high penetration of renewable energy sources(RESs)led to the development of novel markets,whose objective is the harmonization between production and demand,usually noted as real time of flexibility markets.This necessitates the development of novel pricing schemes able to allow energy service providers(ESPs)to maximize their aggregated profits from the traditional markets(trading between wholesale/day-ahead and retail markets)and the innovative flexibility markets.In the same time,ESPs have to offer their end users(consumers)competitive(low cost)energy services.In this context,novel pricing schemes must act,among others,as automated demand side management(DSM)techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves(ECCs)of the consumers.Energy pricing schemes proposed so far,e.g.realtime pricing,interact in an efficient way with wholesale market.But they do not provide consumers with strong enough financial incentives to modify their energy consumption habits towards energy cost curtailment.Thus,they do not interact efficiently with flexibility markets.Therefore,we develop a flexibility real-time pricing(FRTP)scheme,which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs.Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30%more attractive trade-off between the stacked profits of ESPs,i.e.the sum of the profits from retail and flexibility markets,and the satisfaction of consumers.展开更多
This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfish...This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.展开更多
文摘Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.
基金support from the European Union’s Horizon 2020 Research and Innovation Programme for project InComEss under Grant Agreement Number 862597.
文摘In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.
基金funding from the European Union’s Horizon 2020 Research and Innovation Programme(No.731767)in the context of the SOCIALENERGY project.
文摘Liberalized electricity markets,smart grids and high penetration of renewable energy sources(RESs)led to the development of novel markets,whose objective is the harmonization between production and demand,usually noted as real time of flexibility markets.This necessitates the development of novel pricing schemes able to allow energy service providers(ESPs)to maximize their aggregated profits from the traditional markets(trading between wholesale/day-ahead and retail markets)and the innovative flexibility markets.In the same time,ESPs have to offer their end users(consumers)competitive(low cost)energy services.In this context,novel pricing schemes must act,among others,as automated demand side management(DSM)techniques that are able to trigger the desired behavioral changes according to the flexibility market prices in energy consumption curves(ECCs)of the consumers.Energy pricing schemes proposed so far,e.g.realtime pricing,interact in an efficient way with wholesale market.But they do not provide consumers with strong enough financial incentives to modify their energy consumption habits towards energy cost curtailment.Thus,they do not interact efficiently with flexibility markets.Therefore,we develop a flexibility real-time pricing(FRTP)scheme,which offers a dynamically adjustable level of financial incentives to participating users by fairly rewarding the ones that make desirable behavioral changes in their ECCs.Performance evaluation results demonstrate that the proposed FRTP is able to offer a 15%–30%more attractive trade-off between the stacked profits of ESPs,i.e.the sum of the profits from retail and flexibility markets,and the satisfaction of consumers.
基金supported by the European Union’s Horizon 2020 Research and Innovation Program through the SOCIALENERGY Project (No.731767)
文摘This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.
