Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivati...Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.展开更多
The hyperspectral reflectance of the canopy and the leaves on the main stemfor six varieties, two each of rice corn, and cotton crops, were measured at different growth stageswith an ASD FieldSpec Pro FR^(TM) to analy...The hyperspectral reflectance of the canopy and the leaves on the main stemfor six varieties, two each of rice corn, and cotton crops, were measured at different growth stageswith an ASD FieldSpec Pro FR^(TM) to analyze red edge characteristics forleaf area indices (LAI),aboveground biomass, as well as the chlorophyll, carotenoid, and nitrogen content, emphasizingcomparative differences on the red edge parameters. The results showed a 'double peak' phenomenonfor the red edge of the canopy spectra but not for the leaves. There were 'increase' and 'decrease'change rules for the red edge position, lambda_r, the red edge slope, D lambda_r, and the red edgearea, S_r, of the canopy spectra for all 3 crops with a 'blue shift' for lambda_r of the leafspectra for all 3 crops as the development stages progressed. For rice, corn, and cotton the LAI andfresh leaf mass had highly significant correlations (P < 0.01) to the red edge parameters lambda_r,D lambda_r, and S_r of their canopy spectra. Additionally, for all crops the chlorophyll-a,chlorophyll-b, total chlorophyll, and carotenoid content of the leaves all had highly significant (P< 0.01) correlations to their lambda_r. For rice, the nitrogen content of the leaves in g kg^(-1)and phytomassfor a unit area of land in g m^(-2) also had a highly significant (P < 0.01)correlation to lambda_r, D lambda_r lambda_r, and S_r of the canopy spectra.展开更多
The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the opera...The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the operator, as the newest method of the edge detection of a 2-D image, is successfully established. Many experiments haw proved that the algorithm is simpler, more rapid and more precise in location than other edge detection methods. And a schedule of the concrete performance has been given additionally about the edge detection of color images.展开更多
Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers ...Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.展开更多
Anomaly separation using geochemical data often involves operations in the frequency domain, such as filtering and reducing noise/signal ratios. Unfortunately, the abrupt edge truncation of an image along edges and ho...Anomaly separation using geochemical data often involves operations in the frequency domain, such as filtering and reducing noise/signal ratios. Unfortunately, the abrupt edge truncation of an image along edges and holes (with missing data) often causes frequency distribution distortion in the frequency domain. For example, bright strips are commonly seen in frequency distribution when using a Fourier transform. Such edge effect distortion may affect information extraction results; sometimes severely, depending on the edge abruptness of the image. Traditionally, edge effects are reduced by smoothing the image boundary prior to applying a Fourier transform. Zero-padding is one of the most commonly used smoothing methods. This simple method can reduce the edge effect to some degree but still distorts the image in some cases. Moreover, due to the complexity of geoscience images, which can include irregular shapes and holes with missing data, zero-padding does not always give satisfactory results. This paper proposes the use of decay functions to handle edge effects when extracting information from geoscience images. As an application, this method has been used in a newly developed multifractal method (S-A) for separating geochemical anomalies from background patterns. A geochemical dataset chosen from a mineral district in Nova Scotia, Canada was used to validate the method.展开更多
A morphology-based edge detection method has been used to study sea surface temperature (SST) fronts in the Taiwan Strait and its adjacent area. The method is based on mathematical morphology with multi-dimensional an...A morphology-based edge detection method has been used to study sea surface temperature (SST) fronts in the Taiwan Strait and its adjacent area. The method is based on mathematical morphology with multi-dimensional and multi-structural elements. Using six years’ SST data from September 2002 to August 2008, we distinguished the large SST front like Kuroshio Front as well as the smaller ones: namely Taiwan Bank Front, Zhe-Min Coastal Front and Zhang-Yun Ridge Front. The seasonal and monthly variations of these fronts were also studied. Generally, the SST fronts are stronger in winter but weaker in summer. And the fronts are at their active stage during the period from January to May but at their declining stage during the period from July to October.展开更多
Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mo...Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.展开更多
The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload comput...The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。展开更多
基金supported by the National Science and Technology Major Projects (2008ZX05025)the Project of National Oil and Gas Resources Strategic Constituency Survey and Evaluation of the Ministry of Land and Resources,China (XQ-2007-05)
文摘Edge detection and enhancement techniques are commonly used in recognizing the edge of geologic bodies using potential field data. We present a new edge recognition technology based on the normalized vertical derivative of the total horizontal derivative which has the functions of both edge detection and enhancement techniques. First, we calculate the total horizontal derivative （THDR） of the potential-field data and then compute the n-order vertical derivative （VDRn） of the THDR. For the n-order vertical derivative, the peak value of total horizontal derivative （PTHDR） is obtained using a threshold value greater than 0. This PTHDR can be used for edge detection. Second, the PTHDR value is divided by the total horizontal derivative and normalized by the maximum value. Finally, we used different kinds of numerical models to verify the effectiveness and reliability of the new edge recognition technology.
基金Project supported by the National Natural Science Foundation of China (Nos. 40171065 and 40271078) the National '863' Project of China (Nos. 2002AA243011 and 2002AA130010).
文摘The hyperspectral reflectance of the canopy and the leaves on the main stemfor six varieties, two each of rice corn, and cotton crops, were measured at different growth stageswith an ASD FieldSpec Pro FR^(TM) to analyze red edge characteristics forleaf area indices (LAI),aboveground biomass, as well as the chlorophyll, carotenoid, and nitrogen content, emphasizingcomparative differences on the red edge parameters. The results showed a 'double peak' phenomenonfor the red edge of the canopy spectra but not for the leaves. There were 'increase' and 'decrease'change rules for the red edge position, lambda_r, the red edge slope, D lambda_r, and the red edgearea, S_r, of the canopy spectra for all 3 crops with a 'blue shift' for lambda_r of the leafspectra for all 3 crops as the development stages progressed. For rice, corn, and cotton the LAI andfresh leaf mass had highly significant correlations (P < 0.01) to the red edge parameters lambda_r,D lambda_r, and S_r of their canopy spectra. Additionally, for all crops the chlorophyll-a,chlorophyll-b, total chlorophyll, and carotenoid content of the leaves all had highly significant (P< 0.01) correlations to their lambda_r. For rice, the nitrogen content of the leaves in g kg^(-1)and phytomassfor a unit area of land in g m^(-2) also had a highly significant (P < 0.01)correlation to lambda_r, D lambda_r lambda_r, and S_r of the canopy spectra.
基金the National Natural Science Foundation of China Important Research Foundation of Guangdong Province
文摘The definition of the generalized fuzzy set is presented for the first time, and a generalized fuzzy operator is proposed to transform a generalized fuzzy set into a normal fuzzy set. The algorithm theory of the operator, as the newest method of the edge detection of a 2-D image, is successfully established. Many experiments haw proved that the algorithm is simpler, more rapid and more precise in location than other edge detection methods. And a schedule of the concrete performance has been given additionally about the edge detection of color images.
文摘Mobile Edge Computing(MEC) is an emerging technology in 5G era which enables the provision of the cloud and IT services within the close proximity of mobile subscribers.It allows the availability of the cloud servers inside or adjacent to the base station.The endto-end latency perceived by the mobile user is therefore reduced with the MEC platform.The context-aware services are able to be served by the application developers by leveraging the real time radio access network information from MEC.The MEC additionally enables the compute intensive applications execution in the resource constraint devices with the collaborative computing involving the cloud servers.This paper presents the architectural description of the MEC platform as well as the key functionalities enabling the above features.The relevant state-of-the-art research efforts are then surveyed.The paper finally discusses and identifies the open research challenges of MEC.
文摘Anomaly separation using geochemical data often involves operations in the frequency domain, such as filtering and reducing noise/signal ratios. Unfortunately, the abrupt edge truncation of an image along edges and holes (with missing data) often causes frequency distribution distortion in the frequency domain. For example, bright strips are commonly seen in frequency distribution when using a Fourier transform. Such edge effect distortion may affect information extraction results; sometimes severely, depending on the edge abruptness of the image. Traditionally, edge effects are reduced by smoothing the image boundary prior to applying a Fourier transform. Zero-padding is one of the most commonly used smoothing methods. This simple method can reduce the edge effect to some degree but still distorts the image in some cases. Moreover, due to the complexity of geoscience images, which can include irregular shapes and holes with missing data, zero-padding does not always give satisfactory results. This paper proposes the use of decay functions to handle edge effects when extracting information from geoscience images. As an application, this method has been used in a newly developed multifractal method (S-A) for separating geochemical anomalies from background patterns. A geochemical dataset chosen from a mineral district in Nova Scotia, Canada was used to validate the method.
基金supported by National Basic Research Program of China (Grant Nos. 2007CB411803 and 2009CB421208)National Natural Science Foundation of China (Grant Nos. 40576015, 40821063 and 40810069004)
文摘A morphology-based edge detection method has been used to study sea surface temperature (SST) fronts in the Taiwan Strait and its adjacent area. The method is based on mathematical morphology with multi-dimensional and multi-structural elements. Using six years’ SST data from September 2002 to August 2008, we distinguished the large SST front like Kuroshio Front as well as the smaller ones: namely Taiwan Bank Front, Zhe-Min Coastal Front and Zhang-Yun Ridge Front. The seasonal and monthly variations of these fronts were also studied. Generally, the SST fronts are stronger in winter but weaker in summer. And the fronts are at their active stage during the period from January to May but at their declining stage during the period from July to October.
基金supported by NSFC(No. 61571055)fund of SKL of MMW (No. K201815)Important National Science & Technology Specific Projects(2017ZX03001028)
文摘Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.
基金the National Natural Science Foundation of China under Grants No. 61572440 and No. 61502428the Zhejiang Provincial Natural Science Foundation of China under Grants No. LR16F010003 and No. LY19F020033.
文摘The rapid growth of mobile internet services has yielded a variety of computation-intensive applications such as virtual/augmented reality. Mobile Edge Computing (MEC), which enables mobile terminals to offload computation tasks to servers located at the edge of the cellular networks, has been considered as an efficient approach to relieve the heavy computational burdens and realize an efficient computation offloading. Driven by the consequent requirement for proper resource allocations for computation offloading via MEC, in this paper, we propose a Deep-Q Network (DQN) based task offloading and resource allocation algorithm for the MEC. Specifically, we consider a MEC system in which every mobile terminal has multiple tasks offloaded to the edge server and design a joint task offloading decision and bandwidth allocation optimization to minimize the overall offloading cost in terms of energy cost, computation cost, and delay cost. Although the proposed optimization problem is a mixed integer nonlinear programming in nature, we exploit an emerging DQN technique to solve it. Extensive numerical results show that our proposed DQN-based approach can achieve the near-optimal performance。
