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This asks for the development of intelligent algorithms for routing, information sharing security, novel network paradigms, new services, and advanced techniques for data fusion. Classic Internet approaches need to be revised to address the complex requirements imposed by IoT. In IoT scenarios, a large number of devices – and more in general objects – are seamlessly connected to each another for information sharing through the Internet.ĭue to the huge amount of heterogeneous devices, information sharing among IoT devices is one of the biggest challenges. This has carried out to the evolution of a new emerging paradigm, namely the Internet of Things (IoT). It is then foreseen to have 7 devices per person. In 2020 there will be 50 billions of 'things' connected to the Internet. In recent years, a huge amount of devices are connected to the Internet more than people.

Architecture of a convolutional layer, one of the most important building blocks of deep neural networks A simple, fully-connected, deep neural network with three hidden layersįig. Calabrò, 'Enhancing audio surveillance with Deep Neural Networks' Advanced Video and Signal based Surveillance (AVSS) 2017, Lecceįig. Machine learning is an expression used to describe systems that automatically learn to make predictions about some attributes of a data instance, such as the content of a picture or the evolution of the stock market, from a set of training data.ĭeep learning describes a set of recent advancements in the fields of neural network, specifically referring to architectures and techniques leveraging an high number of hidden layers, thus described as deep neural networks.ĭeep learning currently represents the state of the art in many machine learning tasks, especially in computer vision tasks, where deep architectures obtain super-human performances.ĭeep neural networks have also enabled a number of surprising tasks, such as the generation of images from text descriptions and models that generate new deep architectures.ĬOMLAB’s publications about Deep Learning are:

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