Search results for "Toe"
showing 10 items of 3824 documents
Methodological advances in brain connectivity
2012
Determining how distinct neurons or brain regions are connected and communicate with each other is a crucial point in neuroscience, as it allows to investigate how the functional integration of specialized neural populations enables the emergence of coherent cognitive and behavioral states. The general concept of brain connectivity encompasses different aspects: structural connectivity is related to the description of anatomical pathways and synaptic connections; functional connectivity investigates statistical dependencies between spatially separated brain regions; effective connectivity refers to models aimed at elucidating driver-response relationships. The study of these different modes…
Combining Auto-Encoder with LSTM for WiFi-Based Fingerprint Positioning
2021
Although indoor positioning has long been investigated by various means, its accuracy remains concern. Several recent studies have applied machine learning algorithms to explore wireless fidelity (WiFi)-based positioning. In this paper, we propose a novel deep learning model which concatenates an auto-encoder with a long short term memory (LSTM) network for the purpose of WiFi fingerprint positioning. We first employ an auto-encoder to extract representative latent codes of fingerprints. Such an extraction is proven to be more reliable than simply using a deep neural network to extract representative features since a latent code can be reverted back to its original input. Then, a sequence o…
An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders
2020
In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistica…
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…
Early detection of volcanic hazard by lidar measurement of carbon dioxide
2016
Volcanic gases give information on magmatic processes. In particular, anomalous releases of carbon dioxide precede volcanic eruptions. Up to now, this gas has been measured in volcanic plumes with conventional measurements that imply the severe risks of local sampling and can last many hours. For these reasons and for the great advantages of laser sensing, the thorough development of volcanic lidars has been undertaken at ENEA (Italian National Agency for New Technologies, Energy and Sustainable Economic Development). In fact, lidar profiling allows one to scan remotely volcanic plumes in a fast and continuous way, and with high spatial and temporal resolution. A differential absorption lid…
Nano photoelectron ioniser chip using LaB6 for ambient pressure trace gas detection
2012
A detector including a nanoscaled ioniser chip that surmounts the limitation of conventional photo ionisation detectors is presented. Here, ionisable gaseous substances can be detected by photoelectrons accelerated to the ionisation potential of the incoming gaseous molecules. Thin lanthanum hexaboride (LaB"6) films deposited by pulsed laser technique (PLD) serve as the air stable photocathode material representing the basis of the ioniser chip of the detector. Besides the analysis of the emission behaviour of the photocathode in vacuum and at atmospheric pressure, the detection of different volatile alcohols using the detector with a low-energy UV LED instead of a PID (VUV photon source) w…
Ab initio angle- and energy-resolved photoelectron spectroscopy with time-dependent density-functional theory
2012
We present a time-dependent density-functional method able to describe the photoelectron spectrum of atoms and molecules when excited by laser pulses. This computationally feasible scheme is based on a geometrical partitioning that efficiently gives access to photoelectron spectroscopy in time-dependent density-functional calculations. By using a geometrical approach, we provide a simple description of momentum-resolved photoemission including multiphoton effects. The approach is validated by comparison with results in the literature and exact calculations. Furthermore, we present numerical photoelectron angular distributions for randomly oriented nitrogen molecules in a short near-infrared…
Thin Film Characterisation Using MeV Ion Beams
2009
This chapter focuses on the characterisation of very thin films having thicknesses from a few nanometres to tens of nanometres. The driving force for the ion beam analysis community has mostly been the rapid development of microelectronics — all the elements in new thin SiO2 replacing dielectrics, diffusion barriers, and silicide contacts need to be analysed with a depth resolution even better than a nanometre. This together with new film deposition techniques like atomic layer deposition (ALD) [1] have given a push to the ion beam analysis community to develop new and better techniques using energetic (>0.5 MeV) ion beams.
Graphene as a tunable resistor
2014
We present the design of a graphene-based electronically tuneable microstrip attenuator operating at a frequency of 5 GHz. The use of graphene as a variable resistor is discussed and the modelling of its electromagnetic properties at microwave frequencies is fully addressed. The design of the graphene-based attenuator is described. The structure integrates a patch of graphene, whose characteristics can range from being a fairly good conductor to a highly lossy material, depending on the applied voltage. By applying the proper voltage through two high-impedance bias lines, the surface resistivity of graphene can be modified, thereby changing the insertion loss of the microstrip attenuator.
Metal-support and preparation influence on the structural and electronic properties of gold catalysts
2006
Abstract Nanostructured gold catalysts supported on CeO2 and SiO2 were prepared by the deposition–precipitation (DP) and the solvated metal atom dispersion (SMAD) techniques. The structural and electronic properties of the catalysts were investigated by X-ray diffraction (XRD), X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS). Gold was found as small metal nanoparticles (cluster size ∼2 nm) in the SMAD-prepared samples and in ionic state in the DP catalysts. The catalytic activity of the samples was tested in the reaction of low temperature CO oxidation. Gold nanosized particles in a pure metallic state exhibited a worse catalytic performance, both on ceria and…