Rozprawy doktorskie na temat „Real-time”

Simulation is a good way to gain insight into a system, for example during development, without having to run or build the actual system. This is especially true for real-time systems, which often operate in hazardous environments or control critical entities in the 'real' world, making testing of these systems in their real environment unsafe during development.

When building simulations, one simulator is not likely to fit every type of simulation project. Therefore, different simulators, which focus on different aspects of simulation, are built. The High Level Architecture (HLA) from the Defense Modeling and Simulation Office (DMSO) is an architecture for distributed simulations providing a means to communicate between different simulations.

However, the HLA standard has limitations if viewed from a real-time perspective. For example, there is no built-in support for fault tolerance. In this thesis some of the limitations in HLA are identified and an extended architecture that uses a distributed active real-time database as a way to overcome these limitations is presented. One of the major advantages with this new extended HLA architecture is that it is still compliant with HLA, i.e., no modifications have been made to the HLA interfaces.

In a distributed real-time environment where it is imperative to make correct decisions it is important to have all facts available to make the most accurate decision in a certain situation. An example of such an environment is an Unmanned Aerial Vehicle (UAV) system where several UAVs cooperate to carry out a certain task and the data recorded is analyzed after the completion of the mission. This project aims to define and implement a time series architecture for use together with a distributed real-time database for the ability to store temporal data. The result from this project is a time series (TS) architecture that uses DeeDS, a distributed real-time database, for storage. The TS architecture is used by an application modelled from a UAV scenario for storing temporal data. The temporal data is produced by a simulator. The TS architecture solves the problem of storing temporal data for applications using DeeDS. The TS architecture is also useful as a foundation for integrating time series in DeeDS since it is designed for space efficiency and real-time requirements.

The rapid growth of Internet in size and complexity, and frequent emergence of new network applications have made it necessary to develop techniques that can monitor and control the traffic. Efficient and accurate recognition of traffic is the key to the management in real time. This thesis work accomplishes the performance evaluation and optimization of a traffic recognition tool called Traffic Analyzer Module (TAM) which implements a technique that is based on passively observing and identifying signature patterns of the packet payload at the application layer, says signature-based payload recognition. This technique has two highlighted features. Firstly, in contrast to most of previous works which perform classification with offline trace files; this technique applies in online mode which can identify the traffic in real time. Secondly, instead of packet inspection, this technique adopts flow inspection, i.e. identifying traffic in terms of flows each of which consists of the well-known 5-tuple, which canproduce more accurate and reliable results.

To demonstrate this technique, its throughput is evaluated in online mode within a high bandwidth network. Besides throughput measurement, optimizing the recognition algorithm in order to improve its performance is also a task of this thesis work. The results of performance measurement demonstrate the feasibility and reliability of this technique, as well as indicate some clues for future work.

The field of real-time computer animation is undergoing major changes, and many of the methods used to this point are no longer sufficient to achieve the degree of realism that is desired. There is a need for an animation method that provides greater realism, simpler ways to create animations, and more vivid and lifelike virtual creatures. This thesis suggests the possibility of doing that with a physically-based method, by researching current and alternative solutions, developing an architecture for a physically-based system, and describing an implementation of such a system.

The usage of Wireless Local Area Networks is increasing rapidly throughout the world. The technology today is not quality proof for the market’s demands. We want to be able to completely wireless perform our demands, such as confer via video or IP-telephony. This is what we call multimedia real-time traffic. It may be achieved over the physical infrastructure in some areas with good results. The goal of this Master’s Thesis is to analyze the possibilities and give solutions and suggestions to achieve multimedia over the wireless networks, with emphasis on the protocol Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA).

This Master’s Thesis is a theoretical study and the suggested solutions have not been tested in an actual wireless network. Instead they have been tested by computer simulation to give an indication of improvements. Basic configurations are set to the same as in the IEEE 802.11 standard.

Different methods to reach possible improvements of a WLAN are studied, analyzed and simulated. Such methods are: priority, congestion management and multi-channel protocol. Simulations results show how the priority affects the wireless network and how a multi-channel protocol improves the latency and efficiency of the network. The simulation part is concentrated to show improvements of real-time traffic, which is time sensitive. With a multi- channel protocol the network can allow more users, i.e. more traffic. Also, the network will gain improvement in stability.

With the Internet expansion, real-time transmission over Internet is becoming a new promising application. Successful real-time communication over IP networks requires reasonably reliable, low delay, low loss date transport. Since Internet is a non-synchronous packet switching network, high load and lack of guarantees on data delivery make real-time communication such as Voice and Video over IP a challenging application to become realistic on the Internet.

This thesis work is composed of two parts within real-time voice and video communication: network simulation and measurement on the real Internet. In the network simulation, I investigate the requirement for the network"overprovisioning"in order to reach certain quality-of-service. In the experiments on the real Internet, I simulate real-time transmission with UDP packets along two different traffic routes and analyze the quality-of- service I get in each case.

The overall contribution of this work is: To create scenarios to understand the concept of overprovisioning and how it affects the quality-of-service. To develop a mechanism to measure the quality-of-service for real-time traffic provided by the current best-effort network.

forecasting.

The issue of using data as available in real-time to forecasters, policymakers or financial

markets is an important one which has only recently been taken on board in the empirical

literature. Data available and used in real-time are preliminary and differ from ex-post

revised data, and given that data revisions may be quite substantial, the use of latest

available instead of real-time can substantially affect empirical findings (see, among others,

Croushore’s (2011) survey). Furthermore, as variables are released on different dates

and with varying degrees of publication lags, in order not to disregard timely information,

datasets are characterized by the so-called “ragged-edge”structure problem. Hence, special

econometric frameworks, such as developed by Giannone, Reichlin and Small (2008) must

be used.

The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities over

time and relative importance”, studies the reaction of U.S. Treasury bond yields to real-time

market-based news in the daily flow of macroeconomic releases which provide most of the

relevant information on their fundamentals, i.e. the state of the economy and inflation. We

find that yields react systematically to a set of news consisting of the soft data, which have

very short publication lags, and the most timely hard data, with the employment report

being the most important release. However, sub-samples evidence reveals that parameter

instability in terms of absolute and relative size of yields response to news, as well as

significance, is present. Especially, the often cited dominance to markets of the employment

report has been evolving over time, as the size of the yields reaction to it was steadily

increasing. Moreover, over the recent crisis period there has been an overall switch in the

relative importance of soft and hard data compared to the pre-crisis period, with the latter

becoming more important even if less timely, and the scope of hard data to which markets

react has increased and is more balanced as less concentrated on the employment report.

Markets have become more reactive to news over the recent crisis period, particularly to

hard data. This is a consequence of the fact that in periods of high uncertainty (bad state),

markets starve for information and attach a higher value to the marginal information content

of these news releases.

The second and third Chapters focus on the real-time ability of models to now-and-forecast

in a data-rich environment. It uses an econometric framework, that can deal with large

panels that have a “ragged-edge”structure, and to evaluate the models in real-time, we

constructed a database of vintages for US variables reproducing the exact information that

was available to a real-time forecaster.

The second Chapter, “Real-time nowcasting of GDP: a factor model versus professional

forecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDP

growth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factor

model (DFM) framework which enables to handle large unbalanced datasets as available

in real-time. We track the daily evolution throughout the current and next quarter of the

model nowcasting performance. Similarly to GRS’s pseudo real-time results, we find that

the precision of the nowcasts increases with information releases. Moreover, the Survey of

Professional Forecasters does not carry additional information with respect to the model,

suggesting that the often cited superiority of the former, attributable to judgment, is weak

over our sample. As one moves forward along the real-time data flow, the continuous

updating of the model provides a more precise estimate of current quarter GDP growth and

the Survey of Professional Forecasters becomes stale. These results are robust to the recent

recession period.

The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the ability

of different models, to forecast key real and nominal U.S. monthly macroeconomic variables

in a data-rich environment and from the perspective of a real-time forecaster. Among

the approaches used to forecast in a data-rich environment, we use pooling of bi-variate

forecasts which is an indirect way to exploit large cross-section and the directly pooling of

information using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecasts

combination schemes are used, to overcome the choice of model specification faced by

the practitioner (e.g. which criteria to use to select the parametrization of the model), as

we seek for evidence regarding the performance of a model that is robust across specifications/

combination schemes. Our findings show that predictability of the real variables is

confined over the recent recession/crisis period. This in line with the findings of D’Agostino

and Giannone (2012) over an earlier period, that gains in relative performance of models

using large datasets over univariate models are driven by downturn periods which are characterized

by higher comovements. These results are robust to the combination schemes

or models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectional

information along the real-time data flow also helps over the end of the great moderation period. Since this is a quarterly aggregate proxying the state of the economy,

monthly variables carry information content for GDP. But similarly to the findings for the

monthly variables, predictability, as measured by the gains relative to the naive random

walk model, is higher during crisis/recession period than during tranquil times. Regarding

inflation, results are stable across time, but predictability is mainly found at nowcasting

and forecasting one-month ahead, with the BVAR standing out at nowcasting. The results

show that the forecasting gains at these short horizons stem mainly from exploiting timely

information. The results also show that direct pooling of information using a high dimensional

model (DFM or BVAR) which takes into account the cross-correlation between the

variables and efficiently deals with the “ragged-edge”structure of the dataset, yields more

accurate forecasts than the indirect pooling of bi-variate forecasts/models.
Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished