Maximizing Resource Sharing in WDM Mesh Networks With Path-Based Protection and Sparse OEO Regeneration

We propose a resource-sharing scheme that supports three kinds of sharing scenarios in a WDM mesh network with path-based protection and sparse OEO regeneration. Several approaches are used to maximize the sharing of wavelength-links and OEO regenerators.

Performance Analysis of Sparse Traffic Grooming in WDM Mesh Networks

Sparse traffic grooming is a practical problem to be addressed in heterogeneous multi-vendor optical WDM networks where only some of the optical cross-connects (OXCs) have grooming capabilities. Such a network is called as a sparse grooming network. The sparse grooming problem under dynamic traffic in optical WDM mesh networks is a relatively unexplored problem. In this work, we propose the maximize-lightpath-sharing multi-hop (MLS-MH) grooming algorithm to support dynamic traffic grooming in sparse grooming networks. We also present an analytical model to evaluate the blocking performance of the MLS-MH algorithm. Simulation results show that MLSMH outperforms an existing grooming algorithm, the shortest path single-hop (SPSH) algorithm. The numerical results from analysis show that it matches closely with the simulation. The effect of the number of grooming nodes in the network on the blocking performance is also analyzed.

Anderson-like Transition for a Class of Random Sparse Models in d >= 2 Dimensions

We show that the Kronecker sum of d >= 2 copies of a random one-dimensional sparse model displays a spectral transition of the type predicted by Anderson, from absolutely continuous around the center of the band to pure point around the boundaries. Possible applications to physics and open problems are discussed briefly.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Groundwater recharge estimation in a data sparse arid catchment of westbank. Stima della ricarica delle falde acquifere in bacini aridi e carenti di osservazioni idrometriche: il Darga in Cisgiordania.

Il seguente elaborato è frutto del lavoro di ricerca, della durata di cinque mesi, svolto presso il Department of Catchment Hydrology del centro di ricerca UFZ (Helmholtz-Zentrum für Umweltforschung) con sede in Halle an der Saale, Germania. L’obiettivo della Tesi è la stima della ricarica della falda acquifera in un bacino idrografico sprovvisto di serie di osservazioni idrometriche di lunghezza significativa e caratterizzato da clima arido. Il lavoro di Tesi è stato svolto utilizzando un modello afflussi-deflussi concettualmente basato e spazialmente distribuito. La modellistica idrologica in regioni aride è un tema a cui la comunità scientifica sta dedicando numerosi sforzi di ricerca, presentando infatti ancora numerosi problemi aperti dal punto di vista tecnico-scientifico, ed è di primaria importanza per il sostentamento delle popolazioni che vi abitano. Le condizioni climatiche in queste regioni fanno sì che la falda acquifera superficiale sia la principale fonte di approvvigionamento; una stima affidabile della sua ricarica, nel tempo e nello spazio, permette un corretta gestione delle risorse idriche, senza la quale il fabbisogno idrico di queste popolazioni non potrebbe essere soddisfatto. L’area oggetto di studio è il bacino idrografico Darga, una striscia di terra di circa 74 km2, situata in Cisgiordania, la cui sezione di chiusura si trova a circa 4 kilometri dalla costa del Mar Morto, mentre lo spartiacque a monte, ubicato a Nord-ovest, dista circa 3 kilometri dalla città di Gerusalemme.

Sparse Signal Representation of Ultrasonic Signals for Structural Health Monitoring Applications

Assessment of the integrity of structural components is of great importance for aerospace systems, land and marine transportation, civil infrastructures and other biological and mechanical applications. Guided waves (GWs) based inspections are an attractive mean for structural health monitoring. In this thesis, the study and development of techniques for GW ultrasound signal analysis and compression in the context of non-destructive testing of structures will be presented. In guided wave inspections, it is necessary to address the problem of the dispersion compensation. A signal processing approach based on frequency warping was adopted. Such operator maps the frequencies axis through a function derived by the group velocity of the test material and it is used to remove the dependence on the travelled distance from the acquired signals. Such processing strategy was fruitfully applied for impact location and damage localization tasks in composite and aluminum panels. It has been shown that, basing on this processing tool, low power embedded system for GW structural monitoring can be implemented. Finally, a new procedure based on Compressive Sensing has been developed and applied for data reduction. Such procedure has also a beneficial effect in enhancing the accuracy of structural defects localization. This algorithm uses the convolutive model of the propagation of ultrasonic guided waves which takes advantage of a sparse signal representation in the warped frequency domain. The recovery from the compressed samples is based on an alternating minimization procedure which achieves both an accurate reconstruction of the ultrasonic signal and a precise estimation of waves time of flight. Such information is used to feed hyperbolic or elliptic localization procedures, for accurate impact or damage localization.

Sperimentazione di tecniche SBR nel trattamento "on site delle acque domestiche" per nuclei isolati e case sparse.

Scopo di questo lavoro di tesi è quello di sperimentare “sul campo’’ la validità e l'efficacia di tecniche SBR per il trattamento delle acque domestiche per case sparse e nuclei isolati. Tuttavia quella SBR, pur essendo una tecnica impiantistica, può soddisfare queste esigenze. Grazie alla presenza di un adeguato volume d'accumulo ed al funzionamento ciclico dell'impianto sono annullati tutti i problemi determinati, nei normali impianti in continuo, dalla discontinuità dell'afflusso di reflui (picchi di portata). L'impianto è studiato per ridurre la necessità di manutenzione al minimo: tutte le apparecchiature tecniche sono collocate in un armadio esterno all'impianto ed in vasca ci sono solo le tubazioni. Ciò esclude ogni presenza interna al serbatoio di apparecchiature elettriche e di parti in movimento soggette a usura. Il vantaggio è evidente nella semplificazione delle operazioni di gestione/controllo/manutenzione dell’impianto che non richiedono, di norma, lo svuotamento della cisterna e consentono operazioni più agevoli e sicure.Tutti i movimenti di processo sono supportati da tre sistemi Air Lift azionati da un unico compressore che provvede anche all'immissione dell'ossigeno attraverso l'aeratore tubolare a membrana nella vasca SBR. Il compressore si caratterizza per lunga vita operativa e assoluta silenziosità di funzionamento. Per verificare l’efficacia del processo depurativo indotto dall’impianto SBR installato dalla GreenSolar abbiamo fatto dei prelievi sul liquame in entrata alla camera SBR e su quello in uscita da essa. I campioni così prelevati sono stati analizzati da un laboratorio autorizzato.I valori limite imposti dal DGR n. 1053 sono ampiamente rispettati, tuttavia i risultati ottenuti non sono soddisfacenti relativamente alle potenzialità dell'impianto.

Automated Intervertebral Disc Detection from Low Resolution, Sparse MRI Images for the Planning of Scan Geometries

Robust and accurate identification of intervertebral discs from low resolution, sparse MRI scans is essential for the automated scan planning of the MRI spine scan. This paper presents a graphical model based solution for the detection of both the positions and orientations of intervertebral discs from low resolution, sparse MRI scans. Compared with the existing graphical model based methods, the proposed method does not need a training process using training data and it also has the capability to automatically determine the number of vertebrae visible in the image. Experiments on 25 low resolution, sparse spine MRI data sets verified its performance.

Thin-Film Sparse Boundary Array Design for Passive Acoustic Mapping During Ultrasound Therapy

A new 2-D hydrophone array for ultrasound therapy monitoring is presented, along with a novel algorithm for passive acoustic mapping using a sparse weighted aperture. The array is constructed using existing polyvinylidene fluoride (PVDF) ultrasound sensor technology, and is utilized for its broadband characteristics and its high receive sensitivity. For most 2-D arrays, high-resolution imagery is desired, which requires a large aperture at the cost of a large number of elements. The proposed array's geometry is sparse, with elements only on the boundary of the rectangular aperture. The missing information from the interior is filled in using linear imaging techniques. After receiving acoustic emissions during ultrasound therapy, this algorithm applies an apodization to the sparse aperture to limit side lobes and then reconstructs acoustic activity with high spatiotemporal resolution. Experiments show verification of the theoretical point spread function, and cavitation maps in agar phantoms correspond closely to predicted areas, showing the validity of the array and methodology.

Magnetic resonance imaging based determination of body compartments with the versatile, interactive sparse sampling (VISS) method

To investigate the inhomogeneity of radiofrequency fields at higher field strengths that can interfere with established volumetric methods, in particular for the determination of visceral (VAT) and subcutaneous adipose tissue (SCAT). A versatile, interactive sparse sampling (VISS) method is proposed to determine VAT, SCAT, and also total body volume (TBV).

Asymptotics for Marginal Generalized Linear Models With Sparse Correlations

Marginal generalized linear models can be used for clustered and longitudinal data by fitting a model as if the data were independent and using an empirical estimator of parameter standard errors. We extend this approach to data where the number of observations correlated with a given one grows with sample size and show that parameter estimates are consistent and asymptotically Normal with a slower convergence rate than for independent data, and that an information sandwich variance estimator is consistent. We present two problems that motivated this work, the modelling of patterns of HIV genetic variation and the behavior of clustered data estimators when clusters are large.