Learning-based techniques for lensless reconstruction
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While research on the design of heterogeneous concurrent systems has a long and rich history, a unified design methodology and tool support has not emerged so far, and thus the creation of such systems remains a difficult, time-consuming and error-prone pr ...
This deliverable describes the OpenIoT self-management and optimization framework, in terms of algorithms and mechanisms that it comprises as well as in terms of their implementation over the OpenIoT platform and associated cloud infrastructure. As a first ...
This thesis deals with models and methods for large scale optimization problems; in particular, we focus on decision problems arising in the context of seaport container terminals for the efficient management of terminal operations. Large-scale optimizatio ...
Since the seminal work of Thrun [16], the learning to learn paradigm has been defined as the ability of an agent to improve its performance at each task with experience, with the number of tasks. Within the object categorization domain, the visual learning ...
In dynamic optimization problems, the optimal input profiles are typically obtained using models that predict the system behavior. In practice, however, process models are often inaccurate, and on-line adaptation is required for appropriate prediction and ...
This work presents a synthesis method that leads to the preliminary design of industrial energy systems. Such systems are composed of several technologies that transform, through a set of physical unit operations, raw materials and energy into products and ...
Video surveillance is currently undergoing a rapid growth. However, while thousands of cameras are being installed in public places all over the world, computer programs that could reliably detect and track people in order to analyze their behavior are not ...
State machine replication is a technique for making services fault-tolerant by replicating them over a group of machines. Although the theory of state machine replication has been studied extensively, the engineering challenges of converting a theoretical ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...
Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...