Ambient Intelligence is an active research field that studies embed- ding computing elements into the environment - Weiser's concept of disap- pearing computer. Practical experiments are complex to orchestrate, be...
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Ambient Intelligence is an active research field that studies embed- ding computing elements into the environment - Weiser's concept of disap- pearing computer. Practical experiments are complex to orchestrate, because of the technical expertise required in hardware, software engineering and machine learning. The entry barrier for running such an experiment is very high, requiring coordinated e orts at all levels in a research team, and an actual laboratory with sensors in order to collect the data. In this work, we show how the power of Amazon's EC2 cloud can be lever- aged in conjunction with a record and replay system for sensor data in order to allow researchers from anywhere in the world to run such experiments. The software infrastructure used for the experiments is open-source and freely available at https: // github. com/ ami-lab/ AmI-Platform . This platform is used in the AmI Laboratory (EF 210) of the University Po- litehnica of Bucharest.
To engineer the factory of the future the paper argues for a reference model that is not necessary restricted to the control component, but integrates the physical and human components as well. This is due to the real...
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A series of modelling methodologies based on artificial intelligence tools are applied to solve a complex real-world problem. Neural networks and support vector machines are used as models and differential evolution a...
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The spike-timing-dependent-plasticity (STDP) is a mechanism for adjusting the efficacies of biological synapses that was observed and studied in vitro. However, the STDP effect for natural neurons in vivo is subject o...
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The spike-timing-dependent-plasticity (STDP) is a mechanism for adjusting the efficacies of biological synapses that was observed and studied in vitro. However, the STDP effect for natural neurons in vivo is subject of debate because in several experiments when the neurons are stimulated indirectly by natural paths the STDP effect was insignificant. Starting from these aspects this work studies by simulation the STDP long-term effect on synaptic plasticity in order to determine whether the long-term potentiation (LTP) and the long-term depression (LTD) could compensate each other during long-term activity of the neural network. The results show that for some synapses the weights start to oscillate in small intervals around long term stable values that are different from the limits of the weights variation interval. This behavior is caused by the fact that, indeed, the effects of LTP and LTD compensate each other at certain weight values when the same pattern of the input stimuli is presented repeatedly to the network input. The LTP and LTD compensation that determines long term weights stability to other values than the weight variation limits could improve the sensitivity of the learning process in the biological networks because no neuron specific limitation is introduced.
Depth information from a captured scene can provide a more complete description of objects which can be exploited for recognition purposes. In this paper we propose a new approach to static hand gesture recognition th...
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Depth information from a captured scene can provide a more complete description of objects which can be exploited for recognition purposes. In this paper we propose a new approach to static hand gesture recognition that explores only depth information acquired with a Microsoft Kinect camera. Firstly, the regions of interest are extracted from depth data and a 3D point cloud is created taking into consideration the capture device settings. Then, for each 3D point in the simplified point cloud the local spin image descriptor is computed. The performance of hand pose detection greatly depends on the 3D shape descriptor used. KPCA is used for dimensionality reduction and for noise elimination from obtained spin image histograms. The most relevant features in terms of principal components are served as input for a SVM classifier. Experimental results show a high level of accuracy in recognizing static hand gestures using spin images in 3D point clouds.
Wireless sensors networks is an active research topic. The sensor nodes (i.e. motes) are the main building blocks of these networks. There is a permanent concern for building more and more efficient motes in order to ...
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Wireless sensors networks is an active research topic. The sensor nodes (i.e. motes) are the main building blocks of these networks. There is a permanent concern for building more and more efficient motes in order to satisfy the demanding specifications of a sensor network. This paper introduces a new mote device: aceMOTE which is based on a 32 bit microcontroller. The hardware structure of the mote is presented and a comparison is made with similar popular devices. An experimental analysis is made regarding the energy consumption and the results prove that a 32 bit microcontroller is a viable choice for usage in mote design compared with the more frequently used 8 bit and 16 bit microcontrollers.
In this paper, an adaptive observer is proposed for the joint estimation of states and parameters of a fractional nonlinear system with external perturbations. The convergence of the proposed observer is derived in te...
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In this paper authors will expose a work developed with the aim to submit an educational innovation project proposal to a competitive call for Educational Innovation Projects 2013-2015 of the Education Advisory Servic...
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In this paper authors will expose a work developed with the aim to submit an educational innovation project proposal to a competitive call for Educational Innovation Projects 2013-2015 of the Education Advisory Service of the Basque Country University (UPV/EHU, Spain). This project is being carried out in the computer Structure and computer Architecture sub-module of the Degree in computer Management and Information Systems engineering of the University College of engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU). The project is based on the active learning, more specifically, on cooperative learning. In this paper we have given deeper insight the dependencies between all the subjects belonging to the analyzed sub-module, which is composed of two subjects named computer Structure and computer Architecture. We have included in such analysis a previous subject named Principles of Digital Systems Design, which does not belong to that sub-module, but plays an important role in the acquisition of the knowledge and competencies of the two previously referenced subjects.
Embedded network systems use serial communication protocols and simplified media access control schemes in order to exchange information among several interconnected nodes. Within this paper a generalization of the cl...
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Embedded network systems use serial communication protocols and simplified media access control schemes in order to exchange information among several interconnected nodes. Within this paper a generalization of the classical media access control (MAC) protocols used in small embedded serial networks of microcontrollers is investigated and an implementation platform for testing the proposed MAC schemes is described. Thus, the classic master-slave and token-bus protocols turn out to be just two particular cases of the proposed generalized media access control (GMAC) algorithms: a centralized and a distributed one, respectively. By applying these generalized algorithms in practice, significant increase in data throughput is obtained, as it resulted from the simulation tests and then by practical implementation on a typical embedded network of microcontrollers.
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