Search results for "Network Topology"
showing 10 items of 192 documents
Neural Classification of HEP Experimental Data
2009
High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topol…
Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study
2019
Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, th…
Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators
2021
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…
Distributed Consensus in Networks of Dynamic Agents
2006
Stationary and distributed consensus protocols for a network of n dynamic agents under local information is considered. Consensus must be reached on a group decision value returned by a function of the agents' initial state values. As a main contribution we show that the agents can reach consensus if the value of such a function computed over the agents' state trajectories is time invariant. We use this basic result to introduce a protocol design rule allowing consensus on a quite general set of values. Such a set includes, e.g., any generalized mean of order p of the agents' initial states. We demonstrate that the asymptotical consensus is reached via a Lyapunov approach. Finally we perfor…
Efficient techniques for energy saving in data center networks
2018
Data centers are constructed with a huge number of network devices to support the expanding cloud based services. These devices are used to achieve the highest performance in case of full utilization of the network. However, the peak capacity of the network is rarely reached. Consequently, many devices are set into idle state and cause a huge energy waste leading to a non-proportionality between the network load and the energy consumed. In this paper, we propose a new approach to improve the efficiency of data centers in terms of energy consumption. Our approach exploits the correlation in time of the inter-node communication traffic and some topological features to maximize energy saving w…
Advanced Giant Magnetoresistance (GMR) sensors for Selective-Change Driven (SCD) circuits
2021
Nowadays, bio-inspiration is driving novel sensors designs, beyond vision sensors. By taking advantage of their compatibility with standard CMOS technologies, the integration of giant magneto-resistance (GMR) based magnetic sensors within such event-driven approaches is proposed. With this aim, several topologies of such GMR sensors have been designed, fabricated and characterized. In addition, integrated circuit interfaces of a standard CMOS technology are also proposed. Their suitability for this approach is then demonstrated by means of Cadence IC simulations.
Toward Engineering Chiral Rodlike Metal-Organic Frameworks with Rare Topologies.
2018
The establishment of novel design strategies to target chiral rodlike MOFs, elusively faced until now, is one of the most straightforward manners to widen the scope of MOFs. Here we describe our last advances on the application of the metalloligand design strategy toward the development of efficient routes to obtain chiral rodlike MOFs. To this end, we have used as precursor an enantiopure homochiral hexanuclear wheel (1), derived from the amino acid d-valine, which, after a supramolecular reorganization into a one-dimensional homochiral chain-with the same configuration as 1-led to the formation of a homochiral rodlike MOF (2) exhibiting rare etd topology.
Pattern Matching and Pattern Discovery Algorithms for Protein Topologies
2001
We describe algorithms for pattern-matching and pattern-learning in TOPS diagrams (formal descriptions of protein topologies). These problems can be reduced to checking for subgraph isomorphism and finding maximal common subgraphs in a restricted class of ordered graphs. We have developed a subgraph isomorphism algorithm for ordered graphs, which performs well on the given set of data. The maximal common subgraph problem then is solved by repeated subgraph extension and checking for isomorphisms. Despite its apparent inefficiency, this approach yields an algorithm with time complexity proportional to the number of graphs in the input set and is still practical on the given set of data. As a…
Algorithms for Graph and Network Analysis: Clustering and Search of Motifs in Graphs
2019
In this article we deal with problems that involve the analysis of topology in graphs modeling biological networks. In particular, we consider two important problems: (i) Network clustering, aiming at finding compact subgraphs inside the input graph in order to isolate molecular complexes, and (ii) searching for motifs, i.e., sub-structures repeated in the input network and presenting high significance (e.g., in terms of their frequency). We provide a compact overview of the main techniques proposed in the literature to solve these problems.
Performance comparison of container orchestration platforms with low cost devices in the fog, assisting Internet of Things applications
2020
Abstract In the last decade there has been an increasing interest and demand on the Internet of Things (IoT) and its applications. But, when a high level of computing and/or real time processing is required for these applications, different problems arise due to their requirements. In this context, low cost autonomous and distributed Small Board Computers (SBC) devices, with processing, storage capabilities and wireless communications can assist these IoT networks. Usually, these SBC devices run an operating system based on Linux. In this scenario, container-based technologies and fog computing are an interesting approach and both have led to a new paradigm in how devices cooperate, improvi…