To achieve the important tactical requirement of low probability of intercept (LPI) in the complex radar network, dynamically controlling the emission of the radars is very necessary. A novel radar dwelling time contr...
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Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human acti...
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The Brain Computer Interface technology allows the communication between people and mechanical devices controlled by microprocessors [1] [8]. It translates the human mental activity into device commands. The kernel of...
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ISBN:
(纸本)0889865787
The Brain Computer Interface technology allows the communication between people and mechanical devices controlled by microprocessors [1] [8]. It translates the human mental activity into device commands. The kernel of this technology is an algorithm that takes samples, filters and classifies the electro-encephalographic signal [2] [3] [4] [5]. In this paper, different types of filtering windows are considered, the main objective is to determine the type of window with best results in the discrimination stage, when the user is thinking about different activities;as secondary result the most relevant features are extracted. With an earlier and better discrimination the classifier would be easier to implement, faster and more reliable[14].
In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving...
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In recent years,autonomous driving technology has made good progress,but the noncooperative intelligence of vehicle for autonomous driving still has many technical bottlenecks when facing urban road autonomous driving challenges.V2I(Vehicle-to-Infrastructure)communication is a potential solution to enable cooperative intelligence of vehicles and *** this paper,the RGB-PVRCNN,an environment perception framework,is proposed to improve the environmental awareness of autonomous vehicles at intersections by leveraging V2I communication *** framework integrates vision feature based on *** normal distributions transform(NDT)point cloud registration algorithm is deployed both on onboard and roadside to obtain the position of the autonomous vehicles and to build the local map objects detected by roadside multi-sensor system are sent back to autonomous vehicles to enhance the perception ability of autonomous vehicles for benefiting path planning and traffic efficiency at the *** field-testing results show that our method can effectively extend the environmental perception ability and range of autonomous vehicles at the intersection and outperform the PointPillar algorithm and the VoxelRCNN algorithm in detection accuracy.
In this paper,dynamic neural networks(DNNs) are used as the on-line identifier for a class of nonlinear systems with unknown external disturbance and unknown multiple dead zone *** integrating the novel nonlinear dist...
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ISBN:
(纸本)9781479900305
In this paper,dynamic neural networks(DNNs) are used as the on-line identifier for a class of nonlinear systems with unknown external disturbance and unknown multiple dead zone *** integrating the novel nonlinear disturbance observer with adaptive control algorithms,the parameter coupling problem between unknown dead zone and DNNs can be successfully solved and the multiple disturbances can also be rejected *** the observation error and the identification error can be proved to convergent to ***,by combining with the numerical result of an unmanned aerial vehicle(UAV) model,the effectiveness of theoretical algorithms can be fully verified.
Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, ...
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Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles d...
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Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play core contributions to its success. However, in unsupervised domain adapt...
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Autonomic computing aims to embed automation in IT management software such that it can adapt to changes in the configuration, provisioning, protection, and resource utilization variations of the IT infrastructure at ...
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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
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