Distributed computer systems of microcontrollers with environmental sensors, known as wireless sensor networks, allow real time monitoring of complex events. Over the last ten years this technology is slowly being app...
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In today's highly complex multi-AGV systems key research objective is finding a scheduling and routing policy that avoids deadlock while assuring that vehicle utilization is as high as possible. It is well known t...
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Making use of blockchain in complex projects in ways which scale and keep costs down can lead to very complex architectural patterns. The problem with such patterns is that it is very easy to set up a system that only...
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The presented model of an economic system is the generalization of the Arrow-Debreu model for the dynamics case. The authors use the description techniques to develop an optimal enterprise by taking into account the d...
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Ubiquitous computing in healthcare (ubiquitous healthcare) is a very appealing and beneficial goal. It is a mean to achieve patient-centric healthcare and a mean to improve healthcare in general. PACS system is import...
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A traditional representation of aerodynamic characteristics based on the concept of aerodynamic derivatives becomes inadequate at high angles of attack due to significant dynamic effects generated by separated and vor...
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When tracking people or other moving objects with a mobile robot, detection is the first and most critical step. At first most researchers focused on the tracking algorithms, but recently AdaBoost (supervised machine ...
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This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata d...
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This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde
Autonomous exploration and mapping of indoor environments is important task for building inspections. Mapping of large environments in 3D requires high memory and computational consumptions. In this paper, we present ...
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