Spectral Sensing of Adaptive Frequency Hopping Signal for Cognitive Radio

The issue of dynamic spectrum scene analysis in any cognitive radio network becomes extremely complex when low probability of intercept, spread spectrum systems are present in environment. The detection and estimation become more complex if frequency hopping spread spectrum is adaptive in nature. In this paper, we propose two phase approach for detection and estimation of frequency hoping signals. Polyphase filter bank has been proposed as the architecture of choice for detection phase to efficiently detect the presence of frequency hopping signal. Based on the modeling of frequency hopping signal it can be shown that parametric methods of line spectral analysis are well suited for estimation of frequency hopping signals if the issues of order estimation and time localization are resolved. An algorithm using line spectra parameter estimation and wavelet based transient detection has been proposed which resolves above issues in computationally efficient manner suitable for implementation in cognitive radio. The simulations show promising results proving that adaptive frequency hopping signals can be detected and demodulated in a non cooperative context, even at a very low signal to noise ratio in real time.

Multi-agent Search using Voronoi partitions

This paper addresses the problem of multiagent search in an unknown environment. The agents are autonomous in nature and are equipped with necessary sensors to carry out the search operation. The uncertainty, or lack of information about the search area is known a priori as a probability density function. The agents are deployed in an optimal way so as to maximize the one step uncertainty reduction. The agents continue to deploy themselves and reduce uncertainty till the uncertainty density is reduced over the search space below a minimum acceptable level. It has been shown, using LaSalle’s invariance principle, that a distributed control law which moves each of the agents towards the centroid of its Voronoi partition, modified by the sensor range leads to single step optimal deployment. This principle is now used to devise search trajectories for the agents. The simulations were carried out in 2D space with saturation on speeds of the agents. The results show that the control strategy per step indeed moves the agents to the respective centroid and the algorithm reduces the uncertainty distribution to the required level within a few steps.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

On the ultrafast charge migration and subsequent charge directed reactivity in Cl center dot center dot center dot N halogen-bonded clusters following vertical ionization

In this article, we have presented ultrafast charge transfer dynamics through halogen bonds following vertical ionization of representative halogen bonded clusters. Subsequent hole directed reactivity of the radical cations of halogen bonded clusters is also discussed. Furthermore, we have examined effect of the halogen bond strength on the electron-electron correlation-and relaxation-driven charge migration in halogen bonded complexes. For this study, we have selected A-Cl (A represents F, OH, CN, NH2, CF3, and COOH substituents) molecules paired with NH3 (referred as ACl:NH3 complex): these complexes exhibit halogen bonds. To the best of our knowledge, this is the first report on purely electron correlation-and relaxation-driven ultrafast (attosecond) charge migration dynamics through halogen bonds. Both density functional theory and complete active space self-consistent field theory with 6-31+G(d, p) basis set are employed for this work. Upon vertical ionization of NCCl center dot center dot center dot NH3 complex, the hole is predicted to migrate from the NH3-end to the ClCN-end of the NCCl center dot center dot center dot NH3 complex in approximately 0.5 fs on the D-0 cationic surface. This hole migration leads to structural rearrangement of the halogen bonded complex, yielding hydrogen bonding interaction stronger than the halogen bonding interaction on the same cationic surface. Other halogen bonded complexes, such as H2NCl:NH3, F3CCl:NH3, and HOOCCl:NH3, exhibit similar charge migration following vertical ionization. On the contrary, FCl:NH3 and HOCl:NH3 complexes do not exhibit any charge migration following vertical ionization to the D-0 cation state, pointing to interesting halogen bond strength-dependent charge migration. (C) 2015 AIP Publishing LLC.

Al激光等离子体电子密度的空间分辨诊断

采用20μm的狭缝配平面晶体谱仪构成空间分辨光谱测量系统，对Al激光等离子体的K壳层发射谱进行测量。利用Al的Ly-α线谱的翼部Stark展宽效应推得电子密度空间分布轮廓．建立了翼部Stark展宽法测量高密度等离子体电子密度的诊断技术。

Nonparametric mixtures of factor analyzers

The mixtures of factor analyzers (MFA) model allows data to be modeled as a mixture of Gaussians with a reduced parametrization. We present the formulation of a nonparametric form of the MFA model, the Dirichlet process MFA (DPMFA). The proposed model can be used for density estimation or clustering of high dimensiona data. We utilize the DPMFA for clustering the action potentials of different neurons from extracellular recordings, a problem known as spike sorting. DPMFA model is compared to Dirichlet process mixtures of Gaussians model (DPGMM) which has a higher computational complexity. We show that DPMFA has similar modeling performance in lower dimensions when compared to DPGMM, and is able to work in higher dimensions. ©2009 IEEE.

Warped Mixtures for Nonparametric Cluster Shapes

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allows us to summarize the properties of the high-dimensional clusters (or density manifolds) describing the data. The number of manifolds, as well as the shape and dimension of each manifold is automatically inferred. We derive a simple inference scheme for this model which analytically integrates out both the mixture parameters and the warping function. We show that our model is effective for density estimation, performs better than infinite Gaussian mixture models at recovering the true number of clusters, and produces interpretable summaries of high-dimensional datasets.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Franz-Keldysh effect and dynamical Franz-Keldysh effect of cylindrical quantum wires

We investigate the interband optical absorption spectra near the band edge of a cylindrical semiconductor quantum wire in the presence of a static electric field and a terahertz electric field polarized along the axis. Optical absorption spectra are nonperturbatively calculated by solving the low-density semiconductor Bloch equations in real space and real time. The influence of the Franz-Keldysh (FK) effect and dynamical FK effect on the absorption spectrum is investigated. To highlight the physics behind the FK effect and dynamical FK effect, the spatiotemporal dynamics of the polarization wave packet are also presented. Under a reasonable static electric field, substantial and tunable absorption oscillations appear above the band gap. A terahertz field, however, will cause the Autler-Townes splitting of the main exciton peak and the emergence of multiphoton replicas. The presented results suggest that semiconductor quantum wires have potential applications in electro-optical devices.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

Spontaneous spin polarization in geometrically constricted metal nanowires

The electronic structure of thin conducting wires with a narrow geometric constriction has been determined by density-functional theory computations in the local spin density approximation. Spontaneous spin polarization arises in nominally paramagnetic wires at sufficiently low density (r(s)>= 15). Real-space spin-polarization maps show a fascinating variety of magnetic structures pinned at the constriction. The frequency-dependent conductivity is different for the spin-up and spin-down channels and significantly lower than in wires of identically vanishing spin polarization.

An invasive-native mammalian species replacement process captured by camera trap survey random encounter models

Camera traps are used to estimate densities or abundances using capture-recapture and, more recently, random encounter models (REMs). We deploy REMs to describe an invasive-native species replacement process, and to demonstrate their wider application beyond abundance estimation. The Irish hare Lepus timidus hibernicus is a high priority endemic of conservation concern. It is threatened by an expanding population of non-native, European hares L. europaeus, an invasive species of global importance. Camera traps were deployed in thirteen 1 km squares, wherein the ratio of invader to native densities were corroborated by night-driven line transect distance sampling throughout the study area of 1652 km2. Spatial patterns of invasive and native densities between the invader’s core and peripheral ranges, and native allopatry, were comparable between methods. Native densities in the peripheral range were comparable to those in native allopatry using REM, or marginally depressed using Distance Sampling. Numbers of the invader were substantially higher than the native in the core range, irrespective of method, with a 5:1 invader-to-native ratio indicating species replacement. We also describe a post hoc optimization protocol for REM which will inform subsequent (re-)surveys, allowing survey effort (camera hours) to be reduced by up to 57% without compromising the width of confidence intervals associated with density estimates. This approach will form the basis of a more cost-effective means of surveillance and monitoring for both the endemic and invasive species. The European hare undoubtedly represents a significant threat to the endemic Irish hare.

Régulation de l'activité transcriptionnelle de PPARgamma via l'activation des récepteurs CD36 et GHS-R1a : potentiel anti-athérosclérotique

Les sécrétines peptidiques de l’hormone de croissance (GHRPs) constituent une classe de peptides synthétiques capables de stimuler la sécrétion de l’hormone de croissance (GH). Cette activité est médiée par leur liaison à un récepteur couplé aux protéines G : le récepteur des sécrétines de l’hormone de croissance (GHS-R1a), identifié subséquemment comme le récepteur de la ghréline. La ghréline est un peptide de 28 acides aminés sécrété principalement par les cellules de la muqueuse de l’estomac, qui exerce de nombreux effets périphériques indépendamment de la sécrétion de l’hormone de croissance. Les effets indépendants de la sécrétion de GH incluent, entre autres, des actions sur le contrôle de la prise de nourriture, le métabolisme énergétique, la fonction cardiaque, le système immunitaire et la prolifération cellulaire. L’étude de la distribution périphérique des sites de liaison des GHRPs nous a permis d’identifier un second site, le CD36, un récepteur scavenger exprimé dans plusieurs tissus dont le myocarde, l’endothélium de la microvasculature et les monocytes/macrophages. Le CD36 exprimé à la surface du macrophage joue un rôle clé dans l’initiation du développement de l’athérosclérose par la liaison et l’internalisation des lipoprotéines de faible densité oxydées (LDLox) dans l’espace sous-endothélial de l’artère. L’hexaréline, un analogue GHRP, a été développé comme agent thérapeutique pour stimuler la sécrétion de l’hormone de croissance par l’hypophyse. Sa propriété de liaison aux récepteurs GHS-R1a et CD36 situés en périphérie et particulièrement sa capacité d’interférer avec la liaison des LDLox par le CD36 nous ont incité à évaluer la capacité de l’hexaréline à moduler le métabolisme lipidique du macrophage. L’objectif principal de ce projet a été de déterminer les effets de l’activation des récepteurs CD36 et GHS-R1a, par l’hexaréline et la ghréline, le ligand endogène du GHS-R1a, sur la physiologie du macrophage et de déterminer son potentiel anti-athérosclérotique. Les résultats montrent premièrement que l’hexaréline et la ghréline augmentent l’expression des transporteurs ABCA1 et ABCG1, impliqués dans le transport inverse du cholestérol, via un mécanisme contrôlé par le récepteur nucléaire PPARγ. La régulation de l’activité transcriptionnelle de PPARγ par l’activation des récepteurs CD36 et GHS-R1a se fait indépendamment de la présence du domaine de liaison du ligand (LBD) de PPARγ et est conséquente de changements dans l’état de phosphorylation de PPARγ. Une étude plus approfondie de la signalisation résultant de la liaison de la ghréline sur le GHS-R1a révèle que PPARγ est activé par un mécanisme de concertation entre les voies de signalisation Gαq/PI3-K/Akt et Fyn/Dok-1/ERK au niveau du macrophage. Le rôle de PPARγ dans la régulation du métabolisme lipidique par l’hexaréline a été démontré par l’utilisation de macrophages de souris hétérozygotes pour le gène de Ppar gamma, qui présentent une forte diminution de l’activation des gènes de la cascade métabolique PPARγ-LXRα-transporteurs ABC en réponse à l’hexaréline. L’injection quotidienne d’hexaréline à un modèle de souris prédisposées au développement de l’athérosclérose, les souris déficientes en apoE sous une diète riche en cholestérol et en lipides, se traduit également en une diminution significative de la présence de lésions athérosclérotiques correspondant à une augmentation de l’expression des gènes cibles de PPARγ et LXRα dans les macrophages péritonéaux provenant des animaux traités à l’hexaréline. L’ensemble des résultats obtenus dans cette thèse identifie certains nouveaux mécanismes impliqués dans la régulation de PPARγ et du métabolisme du cholestérol dans le macrophage via les récepteurs CD36 et GHS-R1a. Ils pourraient servir de cibles thérapeutiques dans une perspective de traitement des maladies cardiovasculaires.

Échantillonnage dynamique de champs markoviens

L'un des modèles d'apprentissage non-supervisé générant le plus de recherche active est la machine de Boltzmann --- en particulier la machine de Boltzmann restreinte, ou RBM. Un aspect important de l'entraînement ainsi que l'exploitation d'un tel modèle est la prise d'échantillons. Deux développements récents, la divergence contrastive persistante rapide (FPCD) et le herding, visent à améliorer cet aspect, se concentrant principalement sur le processus d'apprentissage en tant que tel. Notamment, le herding renonce à obtenir un estimé précis des paramètres de la RBM, définissant plutôt une distribution par un système dynamique guidé par les exemples d'entraînement. Nous généralisons ces idées afin d'obtenir des algorithmes permettant d'exploiter la distribution de probabilités définie par une RBM pré-entraînée, par tirage d'échantillons qui en sont représentatifs, et ce sans que l'ensemble d'entraînement ne soit nécessaire. Nous présentons trois méthodes: la pénalisation d'échantillon (basée sur une intuition théorique) ainsi que la FPCD et le herding utilisant des statistiques constantes pour la phase positive. Ces méthodes définissent des systèmes dynamiques produisant des échantillons ayant les statistiques voulues et nous les évaluons à l'aide d'une méthode d'estimation de densité non-paramétrique. Nous montrons que ces méthodes mixent substantiellement mieux que la méthode conventionnelle, l'échantillonnage de Gibbs.