Characterizing peers communities and dynamics in a P2P live streaming system

Despite the large number of works devoted to understand P2P live streaming applications, most of them put forth so far rely on characterizing the static view of these systems. In this work, we characterize the SopCast, one of the most important P2P live streaming applications. We focus on its dynamics behavior as well as on the community formation phenomena. Our results show that SopCast presents a low overlay topology diameter and low end-to-end shortest path. In fact, diameter is smaller than 6 hops in almost 90 % of the observation time. More than 96 % of peers’ end-to-end connections present only 3 hops. These values combined may lead to low latencies and a fast streaming diffusion. Second, we show that communities in SopCast are well defined by the streaming data exchange process. Moreover, the SopCast protocol does not group peers according to their Autonomous System. In fact, the probability that a community contains 50 % of its members belonging to the same AS (when we observe the largest AS of our experiments) is lower then 10 %. Peers exchange more data with partners belonging to the same community instead of peers inside the same AS. For the largest AS we have, less than 18 % of peer traffic has been exchanged with another AS partners. Finally, our analysis provides important information to support the future design of more efficient P2P live streaming systems and new protocols that exploit communities’ relationships.     

A Strategy of Density-Based Partitioning for Scalability Problem in Network Self-management

A density-based partitioning strategy is proposed for large domain networks in order to deal with the scalability issue found in autonomic networks considering, as a scenario, the autonomic Quality of Service （QoS） management context. The approach adopted focus as on obtaining dense network partitions having more paths for a given vertices set in the domain. It is demonstrated that dense partitions improve autonomic processing scalability, for instance, reducing routing process complexity. The solution looks for a significant trade-off between partition autonomic algorithm execution time and path selection quality in large domains. Simulation scenarios for path selection execution time are presented and discussed. Authors argue that autonomic networks may benefit from the dense partition approach proposed by achieving scalable, efficient and near real-time support for autonomic management systems.     

3-D Unitary ESPRIT: Accurate attitude estimation for unmanned aerial vehicles with a hexagon-shaped ESPAR array

Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs via data fusion. In this paper, we therefore propose an attitude estimation system based on a hexagon-shaped 7-element electronically steerable parasitic antenna radiator (ESPAR) array. The ESPAR array is well-suited for installment in the UAVs with broad wings and short bodies. Our proposed solution returns an estimation for the pitch and roll based on the inter-element phase delay estimates of the line-of-sight path of the impinging signal over the antenna array. By exploiting the parallel and centrosymmetric structure in the hexagon-shaped ESPAR array, the 3-dimensional Unitary ESPRIT algorithm is applied for phase delay estimation to achieve high accuracy as well as computational efficiency. We devise an attitude estimation algorithm by exploiting the geometrical relationship between the UAV attitude and the estimated phase delays. An analytical closed-form expression of the attitude estimates is obtained by solving the established simultaneous nonlinear equations. Simulations results show the feasibility of our proposed solution for different signal-to-noise ratio levels as well as multipath scenarios.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

Flatness-Based Control of a Single Qubit Gate

This work considers the open-loop control problem of steering a two-level quantum system from any initial to any final condition. The model of this system evolves on the state space , having two inputs that correspond to the complex amplitude of a resonant laser field. A symmetry preserving flat output is constructed using a fully geometric construction and quaternion computations. Simulation results of this flatness-based open-loop control are provided.     

A novel electromyographic signal simulator for muscle contraction studies

Mathematical simulation has been widely used in biomedical and biological sciences. In the case of the surface electromyographic (SEMG) activity, some models have been proposed aiming to study muscle contraction strategies that are used during different tasks and conditions. Most of SEMG simulators are based on energy modulation of a Gaussian noise. This work proposes a novel simulator in which the user-defined parameters are associated with the motor units (MUs) recruitment and their firing rate. Comparison between the mean spectrum of real SEMG signals collected in isometric contraction of the muscle biceps brachii and the mean spectrum obtained from simulated SEMG signals showed a good agreement, pointing the proposed simulator seems to be capable to generate consistent electromyographic signals in time and frequency domains and that can be used in many studies, in particular in the evaluation of automatic methods aimed to detect muscular contraction.     

A Genre-Aware Approach to Focused Crawling

Focused crawlers have as their main goal to crawl Web pages that are relevant to a specific topic or user interest, playing an important role for a great variety of applications. In general, they work by trying to find and crawl all kinds of pages deemed as related to an implicitly declared topic. However, users are often not simply interested in any document about a topic, but instead they may want only documents of a given type or genre on that topic to be retrieved. In this article, we describe an approach to focused crawling that exploits not only content-related information but also genre information present in Web pages to guide the crawling process. This approach has been designed to address situations in which the specific topic of interest can be expressed by specifying two sets of terms, the first describing genre aspects of the desired pages and the second related to the subject or content of these pages, thus requiring no training or any kind of preprocessing. The effectiveness, efficiency and scalability of the proposed approach are demonstrated by a set of experiments involving the crawling of pages related to syllabi of computer science courses, job offers in the computer science field and sale offers of computer equipments. These experiments show that focused crawlers constructed according to our genre-aware approach achieve levels of F1 superior to 88%, requiring the analysis of no more than 65% of the visited pages in order to find 90% of the relevant pages. In addition, we experimentally analyze the impact of term selection on our approach and evaluate a proposed strategy for semi-automatic generation of such terms. This analysis shows that a small set of terms selected by an expert or a set of terms specified by a typical user familiar with the topic is usually enough to produce good results and that such a semi-automatic strategy is very effective in supporting the task of selecting the sets of terms required to guide a crawling process.     

Simple tests for multi-criteria optimality

In this paper we present two tests which can decide whether a given pointx ₀ ^N is locally efficient or not with respect to a given finite set of real valued continuously differentiable functions defined on ^N . Examples indicate that the tests may fail on a nowhere dense set.     

Engineering of protein epitopes: a single deletion in a snake toxin generates full binding capacity to a previously unrecognized antibody

Structural features associated with the ability of a monoclonalantibody (mAb) to discriminate between protein variants areidentified and engineered. The variants are the curaremimetictoxin from Naja nigricollis and erabutoxin a or b from Laticaudasemifasciata which differ from each other by 16 substitutionsand one insertion. The neutralizing mAb M1 recognizes with highaffinity a topographical epitope on the surface of toxin , butfails to recognize the erabutoxins although they possess mostof the residues forming the presumed epitope. Examinations ofthe toxin and erabutoxin 3-D structures and molecular dynamicssimulations reveal several differences between the variants.In particular, the region involving the ß-turn 17–24is organized differently. Analysis of the differences foundin this region suggests that the insertion (or deletion) atposition 18 of the variant amino add sequences is particularlyimportant in determining the differential cross-reactivity.To test this proposal, residue 18 was deleted in one erabutoxinusing sitedirected mutagenesis, and the biological propertiesof the resulting mutant were examined. We found that full antigenicitywas restored in the previously unrecognized variant. The implicationsof this finding are discussed.     

Developed profile of holographically exposed photoresist gratings

A simulation of the profile of holographically recorded structures in photoresists is performed. In addition to its simplicity this simulation can be used to take into account the effects that arise from exposure, photosensitization, development, and resolution of positive photoresists. We analyzed the effects of isotropy of wet development, nonlinearity of the photoresist response curve, background light, and standing waves produced by reflection at the film-substrate interface by using this simulation, and the results agree with the experimentally recorded profiles.