In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on t...
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In this paper we obtain a numerically tractable test (sufficient condition) for the exponential stability of the unique positive equilibrium point of an ODE system. The result (Theorem 3.1) is based on Lyapunov theory...
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Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpeda...
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Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.
For networks of systems, with possibly improper transfer function matrices, we present a design framework which enables H∞ control, while imposing sparsity constraints on the controller’s coprime factors. We propose...
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This work studies the sparse adaptive filter designs in the presence of impulsive disturbance for audio signal recovery. By using the sparse representation of desired signal and compressibility of impulsive disturbanc...
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Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation too...
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Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new sc...
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The analogue implementation of spiking neural networks have the critical advantage of parallel operation and information transmission. The implementation in hardware of the synaptic weights and learning mechanisms rep...
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ISBN:
(数字)9781728198095
ISBN:
(纸本)9781728198101
The analogue implementation of spiking neural networks have the critical advantage of parallel operation and information transmission. The implementation in hardware of the synaptic weights and learning mechanisms represents a challenging task. A cheap and efficient method to store the weights of analogue SNN which benefits from real time adjustment represents the capacitors. However, the main disadvantage of this method represents the leakage currents which make the synaptic weights volatile. This paper proposes and evaluates experimentally a method for refreshing the weights of the analogue synapses by rhythmic activation of the neurons. The results show that the long term memory of analogue neurons improves significantly if the neurons are activated at certain periods of time. Moreover, in order to obtain non-volatile memory the refresh rate can be determined as a function of the initial value and the variation of the synaptic weights.
Complex 3D and multidimensional medical data require significant computational resources to render high-quality, high-resolution images which provide useful information from the originating data set. As an alternative...
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ISBN:
(数字)9781728188034
ISBN:
(纸本)9781728188041
Complex 3D and multidimensional medical data require significant computational resources to render high-quality, high-resolution images which provide useful information from the originating data set. As an alternative to traditional on-the-fly ray-casting-based generation of such images from volume data, we propose a generative model based on a deep neural network which is continually trainable from data dynamically generated by a GPU-based renderer. When properly trained, the model is capable of independently generating images similar to ones produced by a dedicated renderer, using control parameters commonly encountered in volume rendering engines. The network takes over the task of generating images from such parameters, thereby alleviating the need for high-capability computational resources while at the same time providing images without requiring access to the original data sets. Our model allows the user to generate high-resolution images on low-spec hardware without the need for a GPU-based renderer, and without access to sensitive or protected patient data. Also, the model is exploitable in a manner which allows the fully-interactive exploration of complex volume data sets and the efficient generation of representations of the data using limited computational resources.
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