Zero-conductance resonances and spin-filtering effects in ring conductors subject to Rashba coupling

We investigate the effect of Rashba spin-orbit coupling and of a tunnel barrier on the zero conduc- tance resonances appearing in a one-dimensional conducting Aharonov-Bohm (AB) ring symmet- rically coupled to two leads. The transmission function of the corresponding one-electron problem is derived within the scattering matrix approach and analyzed in the complex energy plane with focus on the role of the tunnel barrier strength on the zero-pole structure characteristic of trans- mission (anti)resonances. The lifting of the real conductance zeros is related to the breaking of the spin-reversal symmetry and time-reversal symmetry of Aharonov-Casher (AC)and AB rings, as well as to rotational symmetry breaking in presence of a tunnel barrier. We show that the polarization direction of transmitted electrons can be controlled via the tunnel barrier strength and discuss a novel spin-filtering design in one-dimensional rings with tunable spin-orbit interaction.Comment: 13 pages, 8 figure

Phase rigidity breaking in open Aharonov-Bohm ring coupled to a cantilever

The conductance and the transmittance phase shifts of a two-terminal Aharonov-Bohm (AB) ring are analyzed in the presence of mechanical displacements due to coupling to an external can- tilever. We show that phase rigidity is broken, even in the linear response regime, by means of inelastic scattering due to phonons. Our device provides a way of observing continuous variation of the transmission phase through a two-terminal nano-electro-mechanical system (NEMS). We also propose measurements of phase shifts as a way to determine the strength of the electron-phonon coupling in NEMS.Comment: 7 pages, 8 figure

Models for Identifying Structures in the Data: A Performance Comparison

This paper reports on the unsupervised analysis of seismic signals recorded in Italy, respectively on the Vesuvius volcano, located in Naples, and on the Stromboli volcano, located North of Eastern Sicily. The Vesuvius dataset is composed of earthquakes and false events like thunders, man-made quarry and undersea explosions. The Stromboli dataset consists of explosion-quakes, landslides and volcanic microtremor signals. The aim of this paper is to apply on these datasets three projection methods, the linear Principal Component Analysis (PCA), the Self-Organizing Map (SOM), and the Curvilinear Component Analysis (CCA), in order to compare their performance. Since these algorithms are well known to be able to exploit structures and organize data providing a clear framework for understanding and interpreting their relationships, this work examines the category of structural information that they can provide on our specific sets. Moreover, the paper suggests a breakthrough in the application area of the SOM, used here for clustering different seismic signals. The results show that, among the three above techniques, SOM better visualizes the complex set of high-dimensional data discovering their intrinsic structure and eventually appropriately clustering the different signal typologies under examination, discriminating the explosionquakes from the landslides and microtremor recorded at the Stromboli volcano, and the earthquakes from natural (thunders) and artificial (quarry blasts and undersea explosions) events recorded at the Vesuvius volcano

Automatic Classification of Seismic Signals at Mt. Vesuvius Volcano, Italy, Using Neural Networks

We present a new strategy for reliable automatic classification of local seismic signals and volcano-tectonic earthquakes (VT). The method is based on a supervised neural network in which a new approach for feature extraction from short period seismic signals is applied. To reduce the number of records required for the analysis we set up a specialized neural classifier, able to distinguish two classes of signals, for each of the selected stations. The neural network architecture is a multilayer perceptron (MLP) with a single hidden layer. Spectral features of the signals and the parameterized attributes of their waveform have been used as input for this network. Feature extraction is done by using both the linear predictor coding technique for computing the spectrograms, and a function of the amplitude for characterizing waveforms. Compared to strategies that use only spectral signatures, the inclusion of properly normalized amplitude features improves the performance of the classifiers, and allows the network to better generalize. To train the MLP network we compared the performance of the quasi-Newton algorithm with the scaled conjugate gradient method. We found that the scaled conjugate gradient approach is the faster of the two, with quite equally good performance. Our method was tested on a dataset recorded by four selected stations of the Mt. Vesuvius monitoring network, for the discrimination of low magnitude VT events and transient signals caused by either artificial (quarry blasts, underwater explosions) and natural (thunder) sources. In this test application we obtained 100% correct classification for one of the possible pairs of signal types (VT versus quarry blasts). Because this method was developed independently of this particular discrimination task, it can be applied to a broad range of other applications

An innovative tsunami detector operating in tsunami generation environment

On August 25th 2007 a tsunami detector installed onboard the multi-parameter observatory GEOSTAR was successfully deployed at 3200 b. s. l. in the Gulf of Cadiz, Portugal. This activity is within the NEAREST EC Project (http://nearest.bo.ismar.cnr.it/ ). Among other deliverables, the NEAREST project will produce and test the basic parts of an operational prototype of a near field tsunami warning system. This system includes an onshore warning centre, based on the geophysical monitoring networks which are already operating, and a tsunami detector deployed on board GEOSTAR at the sea bottom. On land the warning centre is in charge of collecting, integrating, and evaluating data recorded at sea. At the sea bottom data is recorded and processed by an advanced type of tsunami detector which includes: a pressure sensor, a seismometer and two accelerometers. The detector communicates acoustically with a surface buoy in two-way mode. The buoy is equipped with meteo station, GPS and tiltmeter and is connected to a shore station via satellite link. The prototype is designed to operate in tsunami generation areas for detection-warning purpose as well as for scientific measurements. The tsunami detector sends a near real time automatic alert message when a seismic or pressure threshold are exceeded. Pressure signals are processed by the tsunami detection algorithm and the water pressure perturbation caused by the seafloor motion is taken into account. The algorithm is designed to detect small tsunami waves, less than one centimetre, in a very noisy environment. Our objective is to combine a novel approach to the tsunami warning problem, with a study of the coupling between the water column perturbations and sea floor motion, together with the long term monitoring of geophysical, geochemical and oceanographic parameters

Monitoring of a methane-seeping pockmark by cabled benthic observatory (Patras Gulf, Greece)

A new seafloor observatory, the gas monitoring module (GMM), has been developed for continuous and long-term measurements of methane and hydrogen sulphide concentrations in seawater, integrated with temperature (T), pressure (P) and conductivity data at the seafloor. GMM was deployed in April 2004 within an active gas-bearing pockmark in the Gulf of Patras (Greece), at a water depth of 42 m. Through a submarine cable linked to an onshore station, it was possible to remotely check, via direct phone connection, GMM functioning and to receive data in nearreal time. Recordings were carried out in two consecutive campaigns over the periods April–July 2004, and September 2004–January 2005, amounting to a combined dataset of ca. 6.5 months. This represents the first long-term monitoring ever done on gas leakage from pockmarks by means of CH4+H2S+T+P sensors. The results show frequent T and P drops associated with gas peaks, more than 60 events in 6.5 months, likely due to intermittent, pulsation-like seepage. Decreases in temperature in the order of 0.1–1°C (up to 1.7°C) below an ambient T of ca. 17°C (annual average) were associated with short-lived pulses (10–60 min) of increased CH4+H2S concentrations. This seepage “pulsation” can either be an active process driven by pressure build-up in the pockmark sediments, or a passive fluid release due to hydrostatic pressure drops induced by bottom currents cascading into the pockmark depression. Redundancy and comparison of data from different sensors were fundamental to interpret subtle proxy signals of temperature and pressure which would not be understood using only one sensor.Published297-302JCR Journalreserve

Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

GEMS (Gamma Energy Marine Spectrometer) is a prototype of an autonomous radioactivity sensor for underwater measurements, developed in the framework for a development of a submarine telescope for neutrino detection (KM3NeT Design Study Project). The spectrometer is highly sensitive to gamma rays produced by 40K decays but it can detect other natural (e.g., 238U,232Th) and anthropogenic radio-nuclides (e.g., 137Cs). GEMS was firstly tested and calibrated in the laboratory using known sources and it was successfully deployed for a long-term (6 months) monitoring at a depth of 3200 m in the Ionian Sea (Capo Passero, offshore Eastern Sicily). The instrument recorded data for the whole deployment period within the expected specifications. This monitoring provided, for the first time, a continuous time-series of radioactivity in deep-sea.In press4.5. Studi sul degassamento naturale e sui gas petroliferiJCR Journalope

Tsunami Warning prototype in the frame of the EC NEAREST project.

Nell' ambito del progetto NEAREST finanziato dalla EC sono stati sviluppati alcuni elementi di un sistema di allerta per tsunami, fra i quali un prototipo di detector di onde anomale istallato a bordo dell' osservatorio abissale GEOSTAR: l' osservatorio con il detector di onde anomale ha operato per un anno nel Golfo di Cadice, a 3200m di profonditàPublishedSassari1.8. Osservazioni di geofisica ambientaleope

NEMO-SN1 (Western Ionian Sea, off Eastern Sicily): A Cabled Abyssal Observatory with Tsunami Early Warning Capability

The NEMO-SN1 (NEutrino Mediterranean Observatory - Submarine Network 1) seafloor observatory is located in the central Mediterranean, Western Ionian Sea, off Eastern Sicily Island (Southern Italy) at 2100 m water depth, 25 km from the harbour of the city of Catania. It is a prototype of cabled deep-sea multiparameter observatory, and the first operating with real-time data transmission in Europe since 2005. NEMO-SN1 is also the first-established node of EMSO (European Multidisciplinary Seafloor Observatory, http://emso-eu.org), one of the European large-scale research infrastructures. EMSO will address long-term monitoring of environmental processes related to marine ecosystems, climate change and geo-hazards. NEMO-SN1 will perform geophysical and environmental long-term monitoring by acquiring seismological, geomagnetic, gravimetric, accelerometric, physico-oceanographic, hydro-acoustic, bio-acoustic measurements to study earthquake and tsunami generation, and to characterize ambient noise which includes marine mammal sounds, and environmental and anthropogenic sources. NEMO-SN1 is also equipped with a prototype tsunami detector, based on the simultaneous measurement of the seismic and bottom pressure signals and a new high performance tsunami detection algorithm. NEMO-SN1 will be a permanent tsunami early warning node in Western Ionian Sea, an area where very destructive earthquakes have occurred in the past, some of them tsunamigenic (e.g., 1693, M=7.5; 1908, M=7.4). Another important feature of NEMO-SN1 is the installation of a low frequency-high sensibility hydrophone and two (scalar and vector, respectively) magnetometers. The objective is to improve the tsunami detection capability of SN1 through the recognition of tsunami-induced hydro-acoustic and electro-magnetic precursors.SubmittedRhodes, Greece3A. Ambiente Marinorestricte