Maximum-likelihood estimation of specific differential phase and attenuation in rain

Precise estimation of propagation parameters inprecipitation media is of interest to improve the performanceof communications systems and in remote sensing applications.In this paper, we present maximum-likelihood estimators ofspecific attenuation and specific differential phase in rain. Themodel used for obtaining the cited estimators assumes coherentpropagation, reflection symmetry of the medium, and Gaussianstatistics of the scattering matrix measurements. No assumptionsabout the microphysical properties of the medium are needed.The performance of the estimators is evaluated through simulateddata. Results show negligible estimators bias and variances closeto Cramer–Rao bounds.

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Applying geostatistics for investigations of forest ecosystems using remote sensing imagery

Abstract

A Secure and Anonymous Cooperative Sensing Protocol for Cognitive Radio Networks

Spectrum is an essential resource for the provision of mobile services. In order to control and delimit its use, governmental agencies set up regulatory policies. Unfortunately, such policies have led to a deficiency of spectrum as only few frequency bands are left unlicensed, and these are used for the majority of new emerging wireless applications. One promising way to alleviate the spectrum shortage problem is adopting a spectrum sharing paradigm in which frequency bands are used opportunistically. Cognitive radio is the key technology to enable this shift of paradigm.Cognitive radio networks are self-organized systems in which devices cooperate to use those spectrum ranges that are not occupied by licensed users. They carry out spectrum sensing in order to detect vacant channels that can be used for communication. Even though spectrum sensing is an active area of research, an important issue remains unsolved: the secure authentication of sensing reports. Not providing security enables the input of false data in the system thus empowering false results. This paper presents a distributed protocol based on wireless physical layer security, symmetric cryptography and one-way functions that allows determining a final sensing decision from multiple sources in a quick and secure way, as well as it preserves users¿ privacy.

Transparent and secure remote network storage system using an untrusted server

Työssä kehitettin läpinäkyvä Internet Small Computer Systems Interface-verkkolevyä (iSCSI) käyttävä varmistusjärjestelmä. Verkkolevyn sisältö suojattiin asiakaspään salauskerroksella (dm-crypt). Järjestely mahdollisti sen, että verkkolevylle tallennetut varmuuskopiot pysyivät luottamuksellisina, vaikka levypalvelinta tarjoava taho oli joko epäluotettava tai suorastaan vihamielinen. Järjestelmän hyötykäyttöä varten kehitettiin helppokäyttöinen prototyyppisovellus. Järjestelmän riskit ja haavoittuvuudet käytiin läpi ja analysoitiin. Järjestelmälle tehtiin myös karkea kryptoanalyysi sen teknistenominaisuuksien pohjalta. Suorituskykymittaukset tehtiin sekä salatulle että salaamattomalle iSCSI-liikenteelle. Näistä todettiin, että salauksen vaikutus suorituskykyyn oli häviävän pieni jopa 100 megabittiä sekunnissa siirtävillä verkkonopeuksilla. Lisäksi pohdittiin teknologian muita sovelluskohteita ja tulevia tutkimusalueita.

TH17 cells promote microbial killing and innate immune sensing of DNA via interleukin 26.

Interleukin 17-producing helper T cells (TH17 cells) have a major role in protection against infections and in mediating autoimmune diseases, yet the mechanisms involved are incompletely understood. We found that interleukin 26 (IL-26), a human TH17 cell-derived cytokine, is a cationic amphipathic protein that kills extracellular bacteria via membrane-pore formation. Furthermore, TH17 cell-derived IL-26 formed complexes with bacterial DNA and self-DNA released by dying bacteria and host cells. The resulting IL-26-DNA complexes triggered the production of type I interferon by plasmacytoid dendritic cells via activation of Toll-like receptor 9, but independently of the IL-26 receptor. These findings provide insights into the potent antimicrobial and proinflammatory function of TH17 cells by showing that IL-26 is a natural human antimicrobial that promotes immune sensing of bacterial and host cell death.

Autonomy and islands: a global study of the factors that determine island autonomy

Att ett geografiskt territorium uppnår autonomi förklaras ofta av skillnad I etnicitet, språk eller religion. En region som skiljer sig i någon av dessa kulturella aspekter från övriga regioner i ett land anses därför ha större sannolikhet att utveckla autonomi. Pär M. Olausson visar i sin avhandling att kulturella aspekter inte är den viktigaste förklaringen till att vissa öar utvecklar autonomi. Kulturella aspekter är bara en del i förklaringen till varför vissa öar utvecklar autonomi medan andra förblir en integrerad del av moderlandet. Kulturella aspekter måste kombineras med andra aspekter för att utgöra en tillräcklig förklaring till skillnaden i autonomi. Bland de autonoma regionerna i världen idag utgör öar en klar majoritet. Öar anses ofta ha speciella egenskaper som gör att de skiljer sig från fastlandet. Känslan av samhörighet och gemenskap är ofta starkare på öar pga deras ofta isolerade läge vilket i sin tur anses förklara varför många öar utvecklar autonomi. Genom att studera både autonoma och icke-autonoma öar visar Olausson att autonomi kan förklaras av att ön ligger längre än 1 000 km från fastlandet eller att den skiljer sig från fastlandet i någon av de kulturella aspekterna, att den har varit ockuperad av främmande stat och därmed avskiljd från övriga landet eller utgjort en egen stat. Den viktigaste förklaringen till varför vissa öar utvecklar autonomi men inte andra är emellertid öns strategiska betydelse.

Ferrofluid thin films as optical gaussmeters proposed for field and magnetic moment sensing

Ferrofluids belonging to the series, Ni x Fe1-x Fe2O4 and Zn x Fe1-x Fe2O4, were synthesized using cold co-precipitation. Liquid films of these ferrofluids were prepared by encapsulating the ferrofluids in between two optically smooth and ultrasonically cleaned glass plates. Magnetic field induced laser transmission through these ferrofluid films has been investigated. Magnetic field values can be calibrated in terms of output laser power in the low field region in which the variation is linear. This set up can be used as a cheap optical gaussmeter in the low field regime. Using the same set-up, the saturation magnetization of the sample used can also be calculated with a sample that is pre-characterized. Hence both magnetization of the sample, as well as applied magnetic field can be sensed and calculated with a precalibrated sample.

Application of ocean colour data to study the oceanographic and atmospheric features off the southwest coast of India with special reference to upwelling

Satellite remote sensing is being effectively used in monitoring the ocean surface and its overlying atmosphere. Technical growth in the field of satellite sensors has made satellite measurement an inevitable part of oceanographic and atmospheric research. Among the ocean observing sensors, ocean colour sensors make use of visible band of electromagnetic spectrum (shorter wavelength). The use of shorter wavelength ensures fine spatial resolution of these parameters to depict oceanographic and atmospheric characteristics of any region having significant spaio-temporal variability. Off the southwest coast of India is such an area showing very significant spatio-temporal oceanographic and atmospheric variability due to the seasonally reversing surface winds and currents. Consequently, the region is enriched with features like upwelling, sinking, eddies, fronts, etc. Among them, upwelling brings nutrient-rich waters from subsurface layers to surface layers. During this process primary production enhances, which is measured in ocean colour sensors as high values of Chl a. Vertical attenuation depth of incident solar radiation (Kd) and Aerosol Optical Depth (AOD) are another two parameters provided by ocean colour sensors. Kd is also susceptible to undergo significant seasonal variability due to the changes in the content of Chl a in the water column. Moreover, Kd is affected by sediment transport in the upper layers as the region experiences land drainage resulting from copious rainfall. The wide range of variability of wind speed and direction may also influence the aerosol source / transport and consequently AOD. The present doctoral thesis concentrates on the utility of Chl a, Kd and AODprovided by satellite ocean colour sensors to understand oceanographic and atmospheric variability off the southwest coast of India. The thesis is divided into six Chapters with further subdivisions

Aerosol optical depths over North Africa: 2. Modeling and model validation

An operational dust forecasting model is developed by including the Met Office Hadley Centre climate model dust parameterization scheme, within a Met Office regional numerical weather prediction (NWP) model. The model includes parameterizations for dust uplift, dust transport, and dust deposition in six discrete size bins and provides diagnostics such as the aerosol optical depth. The results are compared against surface and satellite remote sensing measurements and against in situ measurements from the Facility for Atmospheric Airborne Measurements for a case study when a strong dust event was forecast. Comparisons are also performed against satellite and surface instrumentation for the entire month of August. The case study shows that this Saharan dust NWP model can provide very good guidance of dust events, as much as 42 h ahead. The analysis of monthly data suggests that the mean and variability in the dust model is also well represented.

Snow products for assimilation and verification

Snow is an important component of the land surface, and the choice of products for assimilation or verification can have a large impact on the surface analysis. This paper introduces the many sources of snow data that are currently available, both in situ and from remote sensing from space, along with some recent developments. Snow extent products are derived from the biggest range of sensors and are the most widely used, while information on snow mass from space is still too error-prone to be used successfully in assimilation schemes.