Dissertations / Theses on the topic 'Distributed filtering'

Distributed estimation in a wireless sensor network has many advantages. It eliminates the need of the centralized knowledge of the measurement model parameters and does not have a single point of failure. Also, any sensor node can be queried to retrieve the state estimate. Furthermore, for high dimensional measurements, local processing of information also results in a significant reduction in the communication overhead. The existing distributed Kalman filtering schemes have good computational and communication efficiency, but do not work well for non-linear and non-Gaussian problems. On the other hand, the distributed particle filtering schemes handle the non-linearities well, but have a much higher computational and communication cost. In this thesis, we propose novel distributed filtering schemes based on the ensemble Kalman filter (EnKF). We consider three forms of the EnKF and express their update equations in an alternative information form. This allows us to use the randomized gossip algorithm to reach consensus on the sufficient statistics and perform local updates. The simulation results show that all three forms of the EnKF have a considerably lower computational cost compared to the particle filters. The results suggest that the proposed distributed schemes outperform the existing state-of-the-art distributed filtering schemes in two scenarios, a) linear measurement model with non-linear state dynamics, and b) high dimensional measurements (model parameters known everywhere in the network) with non-linear measurement model and state dynamics. In both of these scenarios, the proposed schemes achieve an estimation accuracy comparable to the existing state-of-the-art schemes while significantly reducing the communication cost.
L'estimation distribuée dans un réseau de capteurs sans fil possède plusieurs avantages. Elle élimine le besoin d'une connaissance centralisée des paramtres du modèle de mesure et n'apas un point de défaillance unique. Aussi, n'importe quel agent-capteur peut-être consulté pour obtenir une approximation de l'état général. De plus, pour des mesures de hautesdimensions, le calcul local d'informations résulte en une réduction significative des coûts decommunication.Les implémentations courantes du filtre de Kalman sont efficaces sur les plans de lacharge de calcul et de la communication mais ne le sont pas pour les problmes non-linéaires et non-gaussiens. D'un autre cˆoté, les techniques distribuées de filtrage particulaire gèrentavec succès les cas non-linéaires mais sont coûteuses sur les plans de la charge de calcul etde la communication.Dans cette thèse, nous proposons des techniques de filtrage distribué basées sur le filtre de Kalman d'ensemble (FKEn). Nous considérons trois formes du FKEn et exprimonsleurs équations de changement sous une forme alternative. Cela nous permet d'utiliser unalgorithme de gossip aléatoire afin d'atteindre un consensus sur les statistiques suffisantes etcalculer les changements locaux. Les résultats des simulations montrent que les trois formesde FKEn ont une charge de calcul bien moindre que les filtres de particules équivalents. Les rèsultats suggèrent que les techniques de filtrage distribué proposées sont plus efficaces quecelles de pointe pour deux scénarios: a) un modèle de mesure linéaire avec des dynamiques d'états non-linéaires et b) des mesures de hautes dimensions (les paramtres du modèle sontconnus de chaque agent) avec un modle de mesure non-linéaire et des dynamiques d'états non-linéaires. Dans les deux scénarios considérés, les techniques proposées atteignent une précision d'estimation comparable à celle des techniques de pointe tout en réduisant significativement les coûts de communication.

The tendency of connecting the Power Electronic loads and distributed power plants through Power Electronic converters is increasing day by day. These Power Electronic converters and loads are the sources of harmonics and reactive power which greatly affect the performance of the power system network. In a weak power grid, the voltage unbalance and non-sinusoidal regimes are very common. Under such circumstances not only the controllability of the power grid itself but also the controllability of the electronic connected to power system equipment’s is heavily affected. So, power quality of the modern power grid (smart grid) is an important issue to address. To overcome the problem of power quality, recent efforts have been made on active filtering. The active power filters have gained much more attention because of excellent performance to mitigate the harmonic and reactive power issues. But the performance of the active filters depends upon the control theory that is employed to formulate the control algorithm of the active filter. A shunt active power filter with controller based on Instantaneous active and reactive power (p-q) theory has been purposed to verify its performance and ability to compensate the harmonics and reactive power. The advantage of p-q theory is that it is instantaneous and works in time domain. The shunt active power filter connected to AC distribution system in the presence of different shares of Power Electronic loads is investigated. It has been investigated through simulations that even under unbalanced and distorted conditions of AC distribution supply voltage and unbalanced loading, shunt active filter is able to produce the unity power factor and mitigate the harmonics (THD) specified by power quality standards. Matlab/Simulink is used as a simulation tool for the research.

We study distributed estimation of dynamic random fields observed by a sparsely connected network of agents/sensors. The sensors are inexpensive, low power, and they communicate locally and perform computation tasks. In the era of large-scale systems and big data, distributed estimators, yielding robust and reliable field estimates, are capable of significantly reducing the large computation and communication load required by centralized estimators, by running local parallel inference algorithms. The distributed estimators have applications in estimation, for example, of temperature, rainfall or wind-speed over a large geographical area; dynamic states of a power grid; location of a group of cooperating vehicles; or beliefs in social networks. The thesis develops distributed estimators where each sensor reconstructs the estimate of the entire field. Since the local estimators have direct access to only local innovations, local observations or a local state, the agents need a consensus-type step to construct locally an estimate of their global versions. This is akin to what we refer to as distributed dynamic averaging. Dynamic averaged quantities, which we call pseudo-quantities, are then used by the distributed local estimators to yield at each sensor an estimate of the whole field. Using terminology from the literature, we refer to the distributed estimators presented in this thesis as Consensus+Innovations-type Kalman filters. We propose three distinct types of distributed estimators according to the quantity that is dynamically averaged: (1) Pseudo-Innovations Kalman Filter (PIKF), (2) Distributed Information Kalman Filter (DIKF), and (3) Consensus+Innovations Kalman Filter (CIKF). The thesis proves that under minimal assumptions the distributed estimators, PIKF, DIKF and CIKF converge to unbiased and bounded mean-squared error (MSE) distributed estimates of the field. These distributed algorithms exhibit a Network Tracking Capacity (NTC) behavior – the MSE is bounded if the degree of instability of the field dynamics is below a threshold. We derive the threshold for each of the filters. The thesis establishes trade-offs between these three distributed estimators. The NTC of the PIKF depends on the network connectivity only, while the NTC of the DIKF and of the CIKF depend also on the observation models. On the other hand, when all the three estimators converge, numerical simulations show that the DIKF improves 2dB over the PIKF. Since the DIKF uses scalar gains, it is simpler to implement than the CIKF. Of the three estimators, the CIKF provides the best MSE performance using optimized gain matrices, yielding an improvement of 3dB over the DIKF. Keywords: Kalman filter, distributed state estimation, multi-agent networks, sensor networks, distributed algorithms, consensus, innovation, asymptotic convergence, mean-squared error, dynamic averaging, Riccati equation, Lyapunov iterations, distributed signal processing, random dynamical systems.

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Este trabalho trata do problema não-linear de estimação simultânea de parâmetros e estado, em sistemas distribuídos, e ainda do problema de localização de sensores associado. A classe de modelos em que estamos interessados é caracterizada por operadores lineares, não- limitados, densamente definidos e dissipativos. Nossa abordagem aplica técnicas de filtragem linear a uma seqüência de linearizações em torno de trajetórias convenientemente escolhidas. A localização ótima de sensores é feita de modo a minimizar uma medida do erro da estimatição simultânea de parâmetros e estados. A contribuição original desta tese compreende o desenvolvimento de : (1) um algoritmo que realiza simultaneamente a identificação e a filtragem de uma classe de sistemas distribuídos operando em ambiente estocástico, e (2) um esquema eficiente de localização ótima de sensores para o problema acima mencionado. Alguns exemplos simulados são apresentados com o objetivo de ilustrar os resultados aqui desenvolvidos
This thesis deals with the nonlinear problem of simultaneous parameter and state estimation for distributed systems, including the associated optimal sensor location. The class of models under consideration is caracterrized by linear unbounded operators which are densely defined and dissipative. Our approach applies linear filtering techniquess to a sequence of linearizations at suitable trajectories. The optimal sensors location is carried out by minimizing a meassure of the state and parameter estimation error. The contribution of this thesis comprises: (1) an algorithm for simultaneous identification and filtering for a classs of distributed systems operting in a stochastic environment and (2) an efficient optimal sensors location scheme for the above mentioned problem. Some simulated exemples are presented to illustrate the proposed approach.

Electronic mail has become cast and embedded in our everyday lives. Billions of legitimate emails are sent on a daily basis. The widely established underlying infrastructure, its widespread availability as well as its ease of use have all acted as catalysts to such pervasive proliferation. Unfortunately, the same can be alleged about unsolicited bulk email, or rather spam. Various methods, as well as enabling architectures are available to try to mitigate spam permeation. In this respect, this dissertation compliments existing survey work in this area by contributing an extensive literature review of traditional and emerging spam filtering approaches. Techniques, approaches and architectures employed for spam filtering are appraised, critically assessing respective strengths and weaknesses. Velocity, volume and variety are key characteristics of the spam challenge. MapReduce (M/R) has become increasingly popular as an Internet scale, data intensive processing platform. In the context of machine learning based spam filter training, support vector machine (SVM) based techniques have been proven effective. SVM training is however a computationally intensive process. In this dissertation, a M/R based distributed SVM algorithm for scalable spam filter training, designated MRSMO, is presented. By distributing and processing subsets of the training data across multiple participating computing nodes, the distributed SVM reduces spam filter training time significantly. To mitigate the accuracy degradation introduced by the adopted approach, a Resource Description Framework (RDF) based feedback loop is evaluated. Experimental results demonstrate that this improves the accuracy levels of the distributed SVM beyond the original sequential counterpart. Effectively exploiting large scale, ‘Cloud’ based, heterogeneous processing capabilities for M/R in what can be considered a non-deterministic environment requires the consideration of a number of perspectives. In this work, gSched, a Hadoop M/R based, heterogeneous aware task to node matching and allocation scheme is designed. Using MRSMO as a baseline, experimental evaluation indicates that gSched improves on the performance of the out-of-the box Hadoop counterpart in a typical Cloud based infrastructure. The focal contribution to knowledge is a scalable, heterogeneous infrastructure and machine learning based spam filtering scheme, able to capitalize on collaborative accuracy improvements through RDF based, end user feedback. MapReduce based RDF Assisted Distributed SVM for High Throughput Spam Filtering

A smart manufacturing network connects machines via sensing, communication, and actuation networks. The data generated from the networks are used in data-driven modeling and decision-making to improve quality, productivity, and flexibility while reducing the cost. This dissertation focuses on improving the data-driven modeling of the quality-process relationship in smart manufacturing networks. The quality-process variable relationships are important to understand for guiding the quality improvement by optimizing the process variables. However, several challenges emerge. First, the big data sets generated from the manufacturing network may be information-poor for modeling, which may lead to high data transmission and computational loads and redundant data storage. Second, the data generated from connected machines often contain inexplicit similarities due to similar product designs and manufacturing processes. Modeling such inexplicit similarities remains challenging. Third, it is unclear how to select representative data sets for modeling in a manufacturing network setting, considering inexplicit similarities. In this dissertation, a data filtering method is proposed to select a relatively small and informative data subset. Multi-task learning is combined with latent variable decomposition to model multiple connected manufacturing processes that are similar-but-non-identical. A data filtering and modeling framework is also proposed to filter the manufacturing data for manufacturing network modeling adaptively. The proposed methodologies have been validated through simulation and the applications to real manufacturing case studies.
Doctor of Philosophy
The advancement of the Internet-of-Things (IoT) integrates manufacturing processes and equipment into a network. Practitioners analyze and apply the data generated from the network to model the manufacturing network to improve product quality. The data quality directly affects the modeling performance and decision effectiveness. However, the data quality is not well controlled in a manufacturing network setting. In this dissertation, we propose a data quality assurance method, referred to as data filtering. The proposed method selects a data subset from raw data collected from the manufacturing network. The proposed method reduces the complexity in modeling while supporting decision effectiveness. To model the data from multiple similar-but-non-identical manufacturing processes, we propose a latent variable decomposition-based multi-task learning model to study the relationships between the process variables and product quality variable. Lastly, to adaptively determine the appropriate data subset for modeling each process in the manufacturing network, we further proposed an integrated data filtering and modeling framework. The proposed integrated framework improved the modeling performance of data generated by babycare manufacturing and semiconductor manufacturing.

The existance of spam email has gone from a fairly small amounts of afew hundred in the late 1970’s to several billions per day in 2010. Thiscontinually growing problem is of great concern to both businesses andusers alike.One attempt to combat this problem comes with a spam filtering toolcalled TRAP. The primary design goal of TRAP is to enable tracking ofthe reputation of mail senders in a decentralized and distributed fashion.In order for the tool to be useful, it is important that it does not haveany security issues that will let a spammer bypass the protocol or gain areputation that it should not have.As a piece of this puzzle, this thesis makes an analysis of TRAP’s protocoland design in order to find threats and vulnerabilies capable of bypassingthe protocol safeguards. Based on these threats we also evaluate possiblemitigations both by analysis and simulation. We have found that althoughthe protocol was not designed with regards to certain attacks on the systemitself most of the attacks can be fairly easily stopped.The analysis shows that by adding cryptographic defenses to the protocola lot of the threats would be mitigated. In those cases where cryptographywould not suffice it is generally down to sane design choices in the implementationas well as not always trusting that a node is being truthful andfollowing protocol.

In this thesis we develop a rigorous mathematical framework for analyzing and approximating optimal sensor placement problems for distributed parameter systems and apply these results to PDE problems defined by the convection-diffusion equations. The mathematical problem is formulated as a distributed parameter optimal control problem with integral Riccati equations as constraints. In order to prove existence of the optimal sensor network and to construct a framework in which to develop rigorous numerical integration of the Riccati equations, we develop a theory based on Bochner integrable solutions of the Riccati equations. In particular, we focus on $\I_p$-valued continuous solutions of the Bochner integral Riccati equation. We give new results concerning the smoothing effect achieved by multiplying a general strongly continuous mapping by operators in $\I_p$. These smoothing results are essential to the proofs of the existence of Bochner integrable solutions of the Riccati integral equations. We also establish that multiplication of continuous $\I_p$-valued functions improves convergence properties of strongly continuous approximating mappings and specifically approximating $C_0$-semigroups. We develop a Galerkin type numerical scheme for approximating the solutions of the integral Riccati equation and prove convergence of the approximating solutions in the $\I_p$-norm. Numerical examples are given to illustrate the theory.
Ph. D.

The Denial of Service (DoS) attack, including Distributed Denial of Service (DDoS) Attack, has become one of the major threats to the Internet today. The victim's resources are exhausted so that its services are disrupted under the DoS attack. Spoofed packets, in which the source IP addresses are forged, are usually used by attackers to implement the attacks or disguise their actual locations. In this thesis, we investigate DoS attack, analyze some existing defense mechanisms, and compare their strengths and weaknesses. Then, we present a novel Marking-based DDoS Attack Detection and Filtering (MDADF) scheme. The MDADF system can distinguish and filter out spoofed IP packets by maintaining a record of the legitimate users and their markings. The system also functions as a DDoS attack detector. We evaluate the performance of this under various conditions in a simulated environment. The results demonstrate that the system is effective in defending against massive DDoS attacks, even when only 20% of the routers on the Internet participate in the marking process. The system is specially effective against IP-spoofed attacks, which are the most difficult to control, although it works well even under randomized attacks. Moreover, the system detects the occurrence of an attack quite quickly and precisely.

Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algorithm to scale linearly with a growing number of users. We also investigate several different variants of this technique including user and item-based recommendation approaches, correlation and vector-based similarity calculations, and selective down-sampling of user interactions. Finally, we provide an experimental comparison of these techniques on the MovieLens dataset consisting of 10 million movie ratings.

In a commercial Internet environment, the quality of service experienced by a user is critical to competitive advantage and business survivability. The availability and response time of a distributed software service are central components of the overall quality of service provided to users. Traditionally availability is a measure of service down time. Traditionally availability measures the probability that the service will be live and is expressed in terms of failure occurrence and repair or recovery time. Response time is a measure of the time taken from when the service request is made, to when service provision occurs for the user. Deteriorating response time is also a valuable indicator to denial of service attacks which continue to pose a significant threat to service availability. The concept of the service cluster is increasingly being deployed to improve service availability and response time. Cluster processor replication increases service availability. Cluster dispatching of service requests across the replicated cluster processors increases service scalability and therefore response time. This thesis commences with a review of the research and current technology in the area of distributed software service availability. The review aims to identify any deficiencies within that area and propose critical features that mitigate those deficiencies. The three critical features proposed are in relation to user wait time, cluster dispatching, and the trust-based filtering of service requests. The user wait time proposal is that the availability of a distributed service should reflect both liveness probability level and probabalistic user access time of the service. The cluster dispatching proposal is that dispatching processing overhead is a function of the number of Internet Protocol (IP) datagrams/Transport Control Protocol (TCP) segments that are received by the dispatcher in respect of each service request. Consequently the number of IP datagrams/TCP segments should be minimised ideally so that for each incoming service request there is one IP datagram/TCP segment. The trust-based filtering proposal is that the level of trust in respect of each service request should be identified by the service as this is critical in mitigating distributed denial of service attacks - and therefore maximising the availability of the service A conceptual availability model which supports the three critical features within an Internet clustered service environment is then described. The conceptual model proposes an expanded availability definition and then describes the realization of this definition via additional capabilities positioned within the Transport layer of the Internet communication environment. The additional capabilities of this model also facilitate the minimization of cluster dispatcher processing load and the identification by the cluster dispatcher of request trust level. The model is then implemented within the Linux kernel. The implementation involves the addition of several options to the existing TCP specification and also the addition of several functions to the existing Socket API. The implementation is subsequently evaluated in a dispatcher-based clustered service environment.

In a commercial Internet environment, the quality of service experienced by a user is critical to competitive advantage and business survivability. The availability and response time of a distributed software service are central components of the overall quality of service provided to users. Traditionally availability is a measure of service down time. Traditionally availability measures the probability that the service will be live and is expressed in terms of failure occurrence and repair or recovery time. Response time is a measure of the time taken from when the service request is made, to when service provision occurs for the user. Deteriorating response time is also a valuable indicator to denial of service attacks which continue to pose a significant threat to service availability. The concept of the service cluster is increasingly being deployed to improve service availability and response time. Cluster processor replication increases service availability. Cluster dispatching of service requests across the replicated cluster processors increases service scalability and therefore response time. This thesis commences with a review of the research and current technology in the area of distributed software service availability. The review aims to identify any deficiencies within that area and propose critical features that mitigate those deficiencies. The three critical features proposed are in relation to user wait time, cluster dispatching, and the trust-based filtering of service requests. The user wait time proposal is that the availability of a distributed service should reflect both liveness probability level and probabalistic user access time of the service. The cluster dispatching proposal is that dispatching processing overhead is a function of the number of Internet Protocol (IP) datagrams/Transport Control Protocol (TCP) segments that are received by the dispatcher in respect of each service request. Consequently the number of IP datagrams/TCP segments should be minimised ideally so that for each incoming service request there is one IP datagram/TCP segment. The trust-based filtering proposal is that the level of trust in respect of each service request should be identified by the service as this is critical in mitigating distributed denial of service attacks - and therefore maximising the availability of the service A conceptual availability model which supports the three critical features within an Internet clustered service environment is then described. The conceptual model proposes an expanded availability definition and then describes the realization of this definition via additional capabilities positioned within the Transport layer of the Internet communication environment. The additional capabilities of this model also facilitate the minimization of cluster dispatcher processing load and the identification by the cluster dispatcher of request trust level. The model is then implemented within the Linux kernel. The implementation involves the addition of several options to the existing TCP specification and also the addition of several functions to the existing Socket API. The implementation is subsequently evaluated in a dispatcher-based clustered service environment.

The work in this dissertation comprises aeroservoelastic simulation development, two modal filter design case studies and theoretical improvement of the modal filter. The modal filter is made robust to sensor bias. Studies have shown that the states estimated by the modal filter can be integrated into active structural control. The integration of modal filters into aircraft structural control systems is explored. Modal filters require distributed sensing to achieve accurate modal coordinate estimates. Distributed sensing technology has progressed to the point, where it is being tested on aircraft such as Ikhana and the upcoming X-56A. Previously, the modal filter was criticized for requiring too many sensors. It was never assessed for its potential benefits in aircraft control. Therefore it is of practical interest to reinvestigate the modal filter. The first case study shows that under conditions of sensor normality, the modal filter is a Gaussian efficient estimator in an aeroservoelastic environment. This is a fundamental experiment considering the fact that the modal filter has never been tested in the airflow. To perform this case study a linear aeroservoelastic code capable of modeling distributed sensing is developed and experimentally validated. From this code, a computational wing model is fitted with distributed sensing. A modal filtering design methodology is developed and applied. With distributed sensing and modal filtering feedback control is achieved. This is also compared and contrasted with a controller using state-of-the-art accelerometers. In addition, new methods of active shape control are introduced for warping an aeroelastic structure utilizing the modal filter and control surfaces. The next case study takes place in a realistic setting for an aircraft. Flexible aircraft bring challenges to the active control community. Increased gust loads, possibility of flutter, and off-design drag may detrimentally affect performance and safety. Aeroservoelastic tailoring, gust load alleviation (GLA) and active flutter suppression (AFS) may be required on future flexible air vehicles. It is found that modal filters can theoretically support these systems. The aircraft case study identifies additional steps required in the modal filtering design methodology. Distributed sensing, the modal filter and modal reference shape control are demonstrated on the X-56A flutter-unstable simulation model. It is shown that control of deformations at potentially millions of points on an aircraft vehicle can be achieved through control of a few modal coordinates. Finally modal filter robustness is theoretically improved and computationally verified. State-of-the-art modal filters have high bias sensitivity. In fact, this is so critical that state-of-the-art modal filters may never be certified for aircraft implementation. This is especially true within a flight critical control system. The solution to this problem is found through derivation of the robust modal filter. The filter combines good properties of concentration algorithms with robust re-descending M-estimation. A new trim criterion specific to the strain based modal sensing system is derived making the filter robust to asymmetric or leverage point outliers. Robust starts are introduced to improve convergence of the modal estimation system to the globally optimal solution in the presence of 100s of biased fiber optic sensors.

Particle filtering has been a very popular method to solve nonlinear/non-Gaussian state estimation problems for more than twenty years. Particle filters (PFs) have found lots of applications in areas that include nonlinear filtering of noisy signals and data, especially in target tracking. However, implementation of high dimensional PFs in real-time for large-scale problems is a very challenging computational task. Parallel & distributed (P&D) computing is a promising way to deal with the computational challenges of PF methods. The main goal of this dissertation is to develop, implement and evaluate computationally efficient PF algorithms for target tracking, and thereby bring them closer to practical applications. To reach this goal, a number of parallel PF algorithms is designed and implemented using different parallel hardware architectures such as Computer Cluster, Graphics Processing Unit (GPU), and Field-Programmable Gate Array (FPGA). Proposed is an improved PF implementation for computer cluster - the Particle Transfer Algorithm (PTA), which takes advantage of the cluster architecture and outperforms significantly existing algorithms. Also, a novel GPU PF algorithm implementation is designed which is highly efficient for GPU architectures. The proposed algorithm implementations on different parallel computing environments are applied and tested for target tracking problems, such as space object tracking, ground multitarget tracking using image sensor, UAV-multisensor tracking. Comprehensive performance evaluation and comparison of the algorithms for both tracking and computational capabilities is performed. It is demonstrated by the obtained simulation results that the proposed implementations help greatly overcome the computational issues of particle filtering for realistic practical problems.

In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a new paradigm to sense the network environment and could greatly enhance existing network capabilities. While there are some existing works addressing these problems, they all suffer from limitations. Current time synchronization methods are not energy efficient on small wireless devices with low quality oscillators. Existing localization methods do not consider additional sources of information available to nodes in the network such as measurements from accelerometers or models of the shadowing environment in the network. RF tomography has only been examined briefly in such networks, and current algorithms can not handle node mobility and rely on shadowing models that have not been experimentally verified. We address the time synchronization problem by analyzing the characteristics of the clocks in small wireless devices, developing a model for it, and then applying a Kalman filter to track both clock offset and skew. In our investigation into RF tomography, we present a method using a Kalman filter which jointly estimates and tracks static and dynamic objects in the environment. We also use channel measurements collected from a field test of our RF tomography testbed to compare RF shadowing models. For the localization problem, we present two algorithms incorporating additional information for improved localization: one based on a distributed extended Kalman filter that combines local acceleration measurements with signal strength measurements for improved localization, and another that uses a distributed particle filter to incorporate a model of the channel environment.

Thesis (MScEng)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: This thesis describes the development of a distributed control system for SSL soccer robots. The project continues on work done to develop a robotics research platform at Stellenbosch University. The wireless communication system is implemented using Player middleware. This enables high level programming of the robot drivers and communication clients, resulting in an easily modifiable system. The system is developed to be used as either a centralised or decentralised control system. The software of the robot’s motor controller unit is updated to ensure optimal movement. Slippage of the robot’s wheels restricts the robot’s movement capabilities. Trajectory tracking software is developed to ensure that the robot follows the desired trajectory while operating within its physical limits. The distributed control architecture reduces the robots dependency on the wireless network and the off-field computer. The robots are given some autonomy by integrating the navigation and control on the robot self. Kalman filters are designed to estimate the robots translational and rotational velocities. The Kalman filters fuse vision data from an overhead vision system with inertial measurements of an on-board IMU. This ensures reliable and accurate position, orientation and velocity information on the robot. Test results show an improvement in the controller performance as a result of the proposed system.
AFRIKAANSE OPSOMMING: Hierdie tesis beskryf die ontwikkeling van ’n verspreidebeheerstelsel vir SSL sokker robotte. Die projek gaan voort op vorige werk wat gedoen is om ’n robotika navorsingsplatform aan die Universiteit van Stellenbosch te ontwikkel. Die kommunikasiestelsel is geïmplementeer met behulp van Player middelware. Dit stel die robotbeheerders en kommunikasiekliënte in staat om in hoë vlak tale geprogrameer te word. Dit lei tot ’n maklik veranderbare stelsel. Die stelsel is so ontwikkel dat dit gebruik kan word as óf ’n gesentraliseerde of verspreidebeheerstelsel. Die sagteware van die motorbeheer eenheid is opgedateer om optimale robot beweging te verseker. As die robot se wiele gly beperk dit die robot se bewegingsvermoëns. Trajekvolgings sagteware is ontwikkel om te verseker dat die robot die gewenste pad volg, terwyl dit binne sy fisiese operasionele grense bly. Die verspreibeheerargitektuur verminder die robot se afhanklikheid op die kommunikasienetwerk en die sentrale rekenaar. Die robot is ’n mate van outonomie gegee deur die integrasie van die navigasie en beheer op die robot self te doen. Kalman filters is ontwerp om die robot se translasie en rotasie snelhede te beraam. Die Kalman filters kombineer visuele data van ’n oorhoofse visiestelsel met inertia metings van ’n IMU op die robot. Dit verseker betroubare en akkurate posisie, oriëntasie en snelheids inligting. Toetsresultate toon ’n verbetering in die beheervermoë as ’n gevolg van die voorgestelde stelsel.

Discrete-event simulation has become one of the most widely used analysis tools for large-scale, complex and dynamic systems such as supply chains as it can take randomness into account and address very detailed models. However, there are major challenges that are faced in simulating such systems, especially when they are used to support short-term decisions (e.g., operational decisions or maintenance and scheduling decisions considered in this research). First, a detailed simulation requires significant amounts of computation time. Second, given the enormous amount of dynamically-changing data that exists in the system, information needs to be updated wisely in the model in order to prevent unnecessary usage of computing and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not to disrupt a dynamically changing system. To meet this temporal requirement and address the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulation (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a simulation model against available computational resources by incorporating dynamic data into the executing model, which then steers the measurement process for selective data update. To the best of our knowledge, the proposed DDDAMS methodology is one of the first efforts to present a coherent integrated decision making framework for timely planning and control of distributed manufacturing enterprises.To this end, comprehensive system architecture and methodologies are first proposed, where the components include 1) real time DDDAM-Simulation, 2) grid computing modules, 3) Web Service communication server, 4) database, 5) various sensors, and 6) real system. Four algorithms are then developed and embedded into a real-time simulator for enabling its DDDAMS capabilities such as abnormality detection, fidelity selection, fidelity assignment, and prediction and task generation. As part of the developed algorithms, improvements are made to the resampling techniques for sequential Bayesian inferencing, and their performance is benchmarked in terms of their resampling qualities and computational efficiencies. Grid computing and Web Services are used for computational resources management and inter-operable communications among distributed software components, respectively. A prototype of proposed DDDAM-Simulation was successfully implemented for preventive maintenance scheduling and part routing scheduling in a semiconductor manufacturing supply chain, where the results look quite promising.

L'objectif de cette thèse est de développer des algorithmes d'apprentissage adaptés aux grandes masses de données. Dans un premier temps, nous considérons le problème de la classification avec un grand nombre de classes. Afin d'obtenir un algorithme adapté à la grande dimension, nous proposons un algorithme qui transforme le problème multi-classes en un problème de classification binaire que nous sous-échantillonnons de manière drastique. Afin de valider cette méthode, nous fournissons une analyse théorique et expérimentale détaillée.Dans la seconde partie, nous approchons le problème de l'apprentissage sur données distribuées en introduisant un cadre asynchrone pour le traitement des données. Nous appliquons ce cadre à deux applications phares : la factorisation de matrice pour les systèmes de recommandation en grande dimension et la classification binaire
This thesis focuses on developing scalable algorithms for large scale machine learning. In this work, we present two perspectives to handle large data. First, we consider the problem of large-scale multiclass classification. We introduce the task of multiclass classification and the challenge of classifying with a large number of classes. To alleviate these challenges, we propose an algorithm which reduces the original multiclass problem to an equivalent binary one. Based on this reduction technique, we introduce a scalable method to tackle the multiclass classification problem for very large number of classes and perform detailed theoretical and empirical analyses.In the second part, we discuss the problem of distributed machine learning. In this domain, we introduce an asynchronous framework for performing distributed optimization. We present application of the proposed asynchronous framework on two popular domains: matrix factorization for large-scale recommender systems and large-scale binary classification. In the case of matrix factorization, we perform Stochastic Gradient Descent (SGD) in an asynchronous distributed manner. Whereas, in the case of large-scale binary classification we use a variant of SGD which uses variance reduction technique, SVRG as our optimization algorithm

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

The explosive growth of the World-Wide-Web and the emergence of ecommerce are the major two factors that have led to the development of recommender systems (Resnick and Varian, 1997). The main task of recommender systems is to learn from users and recommend items (e.g. information, products or books) that match the users’ personal preferences. Recommender systems have been an active research area for more than a decade. Many different techniques and systems with distinct strengths have been developed to generate better quality recommendations. One of the main factors that affect recommenders’ recommendation quality is the amount of information resources that are available to the recommenders. The main feature of the recommender systems is their ability to make personalised recommendations for different individuals. However, for many ecommerce sites, it is difficult for them to obtain sufficient knowledge about their users. Hence, the recommendations they provided to their users are often poor and not personalised. This information insufficiency problem is commonly referred to as the cold-start problem. Most existing research on recommender systems focus on developing techniques to better utilise the available information resources to achieve better recommendation quality. However, while the amount of available data and information remains insufficient, these techniques can only provide limited improvements to the overall recommendation quality. In this thesis, a novel and intuitive approach towards improving recommendation quality and alleviating the cold-start problem is attempted. This approach is enriching the information resources. It can be easily observed that when there is sufficient information and knowledge base to support recommendation making, even the simplest recommender systems can outperform the sophisticated ones with limited information resources. Two possible strategies are suggested in this thesis to achieve the proposed information enrichment for recommenders: • The first strategy suggests that information resources can be enriched by considering other information or data facets. Specifically, a taxonomy-based recommender, Hybrid Taxonomy Recommender (HTR), is presented in this thesis. HTR exploits the relationship between users’ taxonomic preferences and item preferences from the combination of the widely available product taxonomic information and the existing user rating data, and it then utilises this taxonomic preference to item preference relation to generate high quality recommendations. • The second strategy suggests that information resources can be enriched simply by obtaining information resources from other parties. In this thesis, a distributed recommender framework, Ecommerce-oriented Distributed Recommender System (EDRS), is proposed. The proposed EDRS allows multiple recommenders from different parties (i.e. organisations or ecommerce sites) to share recommendations and information resources with each other in order to improve their recommendation quality. Based on the results obtained from the experiments conducted in this thesis, the proposed systems and techniques have achieved great improvement in both making quality recommendations and alleviating the cold-start problem.

This dissertation consists of two main parts. One is about state estimation with two types of unconventional measurements and the other is about two types of network-induced state estimation problems. The two types of unconventional measurements considered are noise-free measurements and set measurements. State estimation with them has numerous real supports. For state estimation with noisy and noise-free measurements, two sequential forms of the batch linear minimum mean-squared error (LMMSE) estimator are obtained to reduce the computational complexity. Inspired by the estimation with quantized measurements developed by Curry [28], under a Gaussian assumption, the minimum mean-squared error (MMSE) state estimator with point measurements and set measurements of any shape is proposed by discretizing continuous set measurements. State estimation under constraints, which are special cases of the more general framework, has some interesting properties. It is found that under certain conditions, although constraints are indispensable in the evolution of the state, update by treating them as measurements is redundant in filtering. The two types of network-induced estimation problems considered are optimal state estimation in the presence of multiple packet dropouts and optimal distributed estimation fusion with transformed data. An alternative form of LMMSE estimation in the presence of multiple packet dropouts, which can overcome the shortcomings of two existing ones, is proposed first. Then under a Gaussian assumption, the MMSE estimation is also obtained based on a hard decision by comparing the measurements at two consecutive time instants. It is pointed out that if this comparison is legitimate, our simple MMSE solution largely nullifies existing work on this problem. By taking linear transformation of the raw measurements received by each sensor, two optimal distributed fusion algorithms are proposed. In terms of optimality, communication and computational requirements, three nice properties make them attractive.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Anderson, David V.; Committee Member: Ferri, Bonnie H.; Committee Member: Hasler, Paul E.; Committee Member: Kang, Sung Ha; Committee Member: McClellan, James H.; Committee Member: Wolf, Wayne H. Part of the SMARTech Electronic Thesis and Dissertation Collection.

[EN] The field of audio signal processing has undergone a major development in recent years. Both the consumer and professional marketplaces continue to show growth in audio applications such as immersive audio schemes that offer optimal listening experience, intelligent noise reduction in cars or improvements in audio teleconferencing or hearing aids. The development of these applications has a common interest in increasing or improving the number of discrete audio channels, the quality of the audio or the sophistication of the algorithms. This often gives rise to problems of high computational cost, even when using common signal processing algorithms, mainly due to the application of these algorithms to multiple signals with real-time requirements. The field of High Performance Computing (HPC) based on low cost hardware elements is the bridge needed between the computing problems and the real multimedia signals and systems that lead to user's applications. In this sense, the present thesis goes a step further in the development of these systems by using the computational power of General Purpose Graphics Processing Units (GPGPUs) to exploit the inherent parallelism of signal processing for multichannel audio applications. The increase of the computational capacity of the processing devices has been historically linked to the number of transistors in a chip. However, nowadays the improvements in the computational capacity are mainly given by increasing the number of processing units and using parallel processing. The Graphics Processing Units (GPUs), which have now thousands of computing cores, are a representative example. The GPUs were traditionally used to graphic or image processing, but new releases in the GPU programming environments such as CUDA have allowed the use of GPUS for general processing applications. Hence, the use of GPUs is being extended to a wide variety of intensive-computation applications among which audio processing is included. However, the data transactions between the CPU and the GPU and viceversa have questioned the viability of the use of GPUs for audio applications in which real-time interaction between microphones and loudspeakers is required. This is the case of the adaptive filtering applications, where an efficient use of parallel computation in not straightforward. For these reasons, up to the beginning of this thesis, very few publications had dealt with the GPU implementation of real-time acoustic applications based on adaptive filtering. Therefore, this thesis aims to demonstrate that GPUs are totally valid tools to carry out audio applications based on adaptive filtering that require high computational resources. To this end, different adaptive applications in the field of audio processing are studied and performed using GPUs. This manuscript also analyzes and solves possible limitations in each GPU-based implementation both from the acoustic point of view as from the computational point of view.
[ES] El campo de procesado de señales de audio ha experimentado un desarrollo importante en los últimos años. Tanto el mercado de consumo como el profesional siguen mostrando un crecimiento en aplicaciones de audio, tales como: los sistemas de audio inmersivo que ofrecen una experiencia de sonido óptima, los sistemas inteligentes de reducción de ruido en coches o las mejoras en sistemas de teleconferencia o en audífonos. El desarrollo de estas aplicaciones tiene un propósito común de aumentar o mejorar el número de canales de audio, la propia calidad del audio o la sofisticación de los algoritmos. Estas mejoras suelen dar lugar a sistemas de alto coste computacional, incluso usando algoritmos comunes de procesado de señal. Esto se debe principalmente a que los algoritmos se suelen aplicar a sistemas multicanales con requerimientos de procesamiento en tiempo real. El campo de la Computación de Alto Rendimiento basado en elementos hardware de bajo coste es el puente necesario entre los problemas de computación y los sistemas multimedia que dan lugar a aplicaciones de usuario. En este sentido, la presente tesis va un paso más allá en el desarrollo de estos sistemas mediante el uso de la potencia de cálculo de las Unidades de Procesamiento Gráfico (GPU) en aplicaciones de propósito general. Con ello, aprovechamos la inherente capacidad de paralelización que poseen las GPU para procesar señales de audio y obtener aplicaciones de audio multicanal. El aumento de la capacidad computacional de los dispositivos de procesado ha estado vinculado históricamente al número de transistores que había en un chip. Sin embargo, hoy en día, las mejoras en la capacidad computacional se dan principalmente por el aumento del número de unidades de procesado y su uso para el procesado en paralelo. Las GPUs son un ejemplo muy representativo. Hoy en día, las GPUs poseen hasta miles de núcleos de computación. Tradicionalmente, las GPUs se han utilizado para el procesado de gráficos o imágenes. Sin embargo, la aparición de entornos sencillos de programación GPU, como por ejemplo CUDA, han permitido el uso de las GPU para aplicaciones de procesado general. De ese modo, el uso de las GPU se ha extendido a una amplia variedad de aplicaciones que requieren cálculo intensivo. Entre esta gama de aplicaciones, se incluye el procesado de señales de audio. No obstante, las transferencias de datos entre la CPU y la GPU y viceversa pusieron en duda la viabilidad de las GPUs para aplicaciones de audio en las que se requiere una interacción en tiempo real entre micrófonos y altavoces. Este es el caso de las aplicaciones basadas en filtrado adaptativo, donde el uso eficiente de la computación en paralelo no es sencillo. Por estas razones, hasta el comienzo de esta tesis, había muy pocas publicaciones que utilizaran la GPU para implementaciones en tiempo real de aplicaciones acústicas basadas en filtrado adaptativo. A pesar de todo, esta tesis pretende demostrar que las GPU son herramientas totalmente válidas para llevar a cabo aplicaciones de audio basadas en filtrado adaptativo que requieran elevados recursos computacionales. Con este fin, la presente tesis ha estudiado y desarrollado varias aplicaciones adaptativas de procesado de audio utilizando una GPU como procesador. Además, también analiza y resuelve las posibles limitaciones de cada aplicación tanto desde el punto de vista acústico como desde el punto de vista computacional.
[CAT] El camp del processament de senyals d'àudio ha experimentat un desenvolupament important als últims anys. Tant el mercat de consum com el professional segueixen mostrant un creixement en aplicacions d'àudio, com ara: els sistemes d'àudio immersiu que ofereixen una experiència de so òptima, els sistemes intel·ligents de reducció de soroll en els cotxes o les millores en sistemes de teleconferència o en audiòfons. El desenvolupament d'aquestes aplicacions té un propòsit comú d'augmentar o millorar el nombre de canals d'àudio, la pròpia qualitat de l'àudio o la sofisticació dels algorismes que s'utilitzen. Això, sovint dóna lloc a sistemes d'alt cost computacional, fins i tot quan es fan servir algorismes comuns de processat de senyal. Això es deu principalment al fet que els algorismes se solen aplicar a sistemes multicanals amb requeriments de processat en temps real. El camp de la Computació d'Alt Rendiment basat en elements hardware de baix cost és el pont necessari entre els problemes de computació i els sistemes multimèdia que donen lloc a aplicacions d'usuari. En aquest sentit, aquesta tesi va un pas més enllà en el desenvolupament d'aquests sistemes mitjançant l'ús de la potència de càlcul de les Unitats de Processament Gràfic (GPU) en aplicacions de propòsit general. Amb això, s'aprofita la inherent capacitat de paral·lelització que posseeixen les GPUs per processar senyals d'àudio i obtenir aplicacions d'àudio multicanal. L'augment de la capacitat computacional dels dispositius de processat ha estat històricament vinculada al nombre de transistors que hi havia en un xip. No obstant, avui en dia, les millores en la capacitat computacional es donen principalment per l'augment del nombre d'unitats de processat i el seu ús per al processament en paral·lel. Un exemple molt representatiu són les GPU, que avui en dia posseeixen milers de nuclis de computació. Tradicionalment, les GPUs s'han utilitzat per al processat de gràfics o imatges. No obstant, l'aparició d'entorns senzills de programació de la GPU com és CUDA, han permès l'ús de les GPUs per a aplicacions de processat general. D'aquesta manera, l'ús de les GPUs s'ha estès a una àmplia varietat d'aplicacions que requereixen càlcul intensiu. Entre aquesta gamma d'aplicacions, s'inclou el processat de senyals d'àudio. No obstant, les transferències de dades entre la CPU i la GPU i viceversa van posar en dubte la viabilitat de les GPUs per a aplicacions d'àudio en què es requereix la interacció en temps real de micròfons i altaveus. Aquest és el cas de les aplicacions basades en filtrat adaptatiu, on l'ús eficient de la computació en paral·lel no és senzilla. Per aquestes raons, fins al començament d'aquesta tesi, hi havia molt poques publicacions que utilitzessin la GPU per implementar en temps real aplicacions acústiques basades en filtrat adaptatiu. Malgrat tot, aquesta tesi pretén demostrar que les GPU són eines totalment vàlides per dur a terme aplicacions d'àudio basades en filtrat adaptatiu que requereixen alts recursos computacionals. Amb aquesta finalitat, en la present tesi s'han estudiat i desenvolupat diverses aplicacions adaptatives de processament d'àudio utilitzant una GPU com a processador. A més, aquest manuscrit també analitza i resol les possibles limitacions de cada aplicació, tant des del punt de vista acústic, com des del punt de vista computacional.
Lorente Giner, J. (2015). Adaptive signal processing for multichannel sound using high performance computing [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/58427
TESIS

The Queensland University of Technology (QUT) allows the presentation of a thesis for the Degree of Doctor of Philosophy in the format of published or submitted papers, where such papers have been published, accepted or submitted during the period of candidature. This thesis is composed of seven published/submitted papers, of which one has been published, three accepted for publication and the other three are under review. This project is financially supported by an Australian Research Council (ARC) Discovery Grant with the aim of proposing strategies for the performance control of Distributed Generation (DG) system with digital estimation of power system signal parameters. Distributed Generation (DG) has been recently introduced as a new concept for the generation of power and the enhancement of conventionally produced electricity. Global warming issue calls for renewable energy resources in electricity production. Distributed generation based on solar energy (photovoltaic and solar thermal), wind, biomass, mini-hydro along with use of fuel cell and micro turbine will gain substantial momentum in the near future. Technically, DG can be a viable solution for the issue of the integration of renewable or non-conventional energy resources. Basically, DG sources can be connected to local power system through power electronic devices, i.e. inverters or ac-ac converters. The interconnection of DG systems to power system as a compensator or a power source with high quality performance is the main aim of this study. Source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, distortion at the point of common coupling in weak source cases, source current power factor, and synchronism of generated currents or voltages are the issues of concern. The interconnection of DG sources shall be carried out by using power electronics switching devices that inject high frequency components rather than the desired current. Also, noise and harmonic distortions can impact the performance of the control strategies. To be able to mitigate the negative effect of high frequency and harmonic as well as noise distortion to achieve satisfactory performance of DG systems, new methods of signal parameter estimation have been proposed in this thesis. These methods are based on processing the digital samples of power system signals. Thus, proposing advanced techniques for the digital estimation of signal parameters and methods for the generation of DG reference currents using the estimates provided is the targeted scope of this thesis. An introduction to this research – including a description of the research problem, the literature review and an account of the research progress linking the research papers – is presented in Chapter 1. One of the main parameters of a power system signal is its frequency. Phasor Measurement (PM) technique is one of the renowned and advanced techniques used for the estimation of power system frequency. Chapter 2 focuses on an in-depth analysis conducted on the PM technique to reveal its strengths and drawbacks. The analysis will be followed by a new technique proposed to enhance the speed of the PM technique while the input signal is free of even-order harmonics. The other techniques proposed in this thesis as the novel ones will be compared with the PM technique comprehensively studied in Chapter 2. An algorithm based on the concept of Kalman filtering is proposed in Chapter 3. The algorithm is intended to estimate signal parameters like amplitude, frequency and phase angle in the online mode. The Kalman filter is modified to operate on the output signal of a Finite Impulse Response (FIR) filter designed by a plain summation. The frequency estimation unit is independent from the Kalman filter and uses the samples refined by the FIR filter. The frequency estimated is given to the Kalman filter to be used in building the transition matrices. The initial settings for the modified Kalman filter are obtained through a trial and error exercise. Another algorithm again based on the concept of Kalman filtering is proposed in Chapter 4 for the estimation of signal parameters. The Kalman filter is also modified to operate on the output signal of the same FIR filter explained above. Nevertheless, the frequency estimation unit, unlike the one proposed in Chapter 3, is not segregated and it interacts with the Kalman filter. The frequency estimated is given to the Kalman filter and other parameters such as the amplitudes and phase angles estimated by the Kalman filter is taken to the frequency estimation unit. Chapter 5 proposes another algorithm based on the concept of Kalman filtering. This time, the state parameters are obtained through matrix arrangements where the noise level is reduced on the sample vector. The purified state vector is used to obtain a new measurement vector for a basic Kalman filter applied. The Kalman filter used has similar structure to a basic Kalman filter except the initial settings are computed through an extensive math-work with regards to the matrix arrangement utilized. Chapter 6 proposes another algorithm based on the concept of Kalman filtering similar to that of Chapter 3. However, this time the initial settings required for the better performance of the modified Kalman filter are calculated instead of being guessed by trial and error exercises. The simulations results for the parameters of signal estimated are enhanced due to the correct settings applied. Moreover, an enhanced Least Error Square (LES) technique is proposed to take on the estimation when a critical transient is detected in the input signal. In fact, some large, sudden changes in the parameters of the signal at these critical transients are not very well tracked by Kalman filtering. However, the proposed LES technique is found to be much faster in tracking these changes. Therefore, an appropriate combination of the LES and modified Kalman filtering is proposed in Chapter 6. Also, this time the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 7 proposes the other algorithm based on the concept of Kalman filtering similar to those of Chapter 3 and 6. However, this time an optimal digital filter is designed instead of the simple summation FIR filter. New initial settings for the modified Kalman filter are calculated based on the coefficients of the digital filter applied. Also, the ability of the proposed algorithm is verified on the real data obtained from a prototype test object. Chapter 8 uses the estimation algorithm proposed in Chapter 7 for the interconnection scheme of a DG to power network. Robust estimates of the signal amplitudes and phase angles obtained by the estimation approach are used in the reference generation of the compensation scheme. Several simulation tests provided in this chapter show that the proposed scheme can very well handle the source and load unbalance, load non-linearity, interharmonic distortion, supply voltage distortion, and synchronism of generated currents or voltages. The purposed compensation scheme also prevents distortion in voltage at the point of common coupling in weak source cases, balances the source currents, and makes the supply side power factor a desired value.

It is not uncommon for enterprises today to be faced with the demand to integrate and incor- porate many different and possibly heterogeneous systems which are generally independently designed and developed, to allow seamless access. In effect, the integration of these systems results in one large whole system that must be able, at the same time, to maintain the local autonomy and to continue working as an independent entity. This problem has introduced a new distributed architecture called federated systems. The most challenging issue in federated systems is to find answers for the question of how to efficiently cooperate while preserving their autonomous characteristic, especially the security autonomy. This thesis intends to address this issue. The thesis reviews the evolution of the concept of federated systems and discusses the organisational characteristics as well as remaining security issues with the existing approaches. The thesis examines how delegation can be used as means to achieve better security, especially authorisation while maintaining autonomy for the participating member of the federation. A delegation taxonomy is proposed as one of the main contributions. The major contribution of this thesis is to study and design a mechanism to support dele- gation within and between multiple security domains with constraint management capability. A novel delegation framework is proposed including two modules: Delegation Constraint Man- agement module and Policy Management module. The first module is designed to effectively create, track and manage delegation constraints, especially for delegation processes which require re-delegation (indirect delegation). The first module employs two algorithms to trace the root authority of a delegation constraint chain and to prevent the potential conflict when creating a delegation constraint chain if necessary. The first module is designed for conflict prevention not conflict resolution. The second module is designed to support the first module via the policy comparison capability. The major function of this module is to provide the delegation framework the capability to compare policies and constraints (written under the format of a policy). The module is an extension of Lin et al.'s work on policy filtering and policy analysis. Throughout the thesis, some case studies are used as examples to illustrate the discussed concepts. These two modules are designed to capture one of the most important aspects of the delegation process: the relationships between the delegation transactions and the involved constraints, which are not very well addressed by the existing approaches. This contribution is significant because the relationships provide information to keep track and en- force the involved delegation constraints and, therefore, play a vital role in maintaining and enforcing security for transactions across multiple security domains.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Finding faults or causes of performance problems in modernWeb computer systems is an arduous task that involves many hours of system metrics monitoring and log analysis. In order to aid administrators in this task, many anomaly detection mechanisms have been proposed to analyze the behavior of the system by collecting a large volume of statistical information showing the condition and performance of the computer system. One of the approaches adopted by these mechanism is the monitoring through strong correlations found in the system. In this approach, the collection of large amounts of data generate drawbacks associated with communication, storage and specially with the processing of information collected. Nevertheless, few mechanisms for detecting anomalies have a strategy for the selection of statistical information to be collected, i.e., for the selection of monitored metrics. This paper presents three metrics selection filters for mechanisms of anomaly detection based on monitoring of correlations. These filters were based on the concept of partial correlation technique which is capable of providing information not observable by common correlations methods. The validation of these filters was performed on a scenario of Web application, and, to simulate this environment, we use the TPC-W, a Web transactions Benchmark of type E-commerce. The results from our evaluation shows that one of our filters allowed the construction of a monitoring network with 8% fewer metrics that state-of-the-art filters, and achieve fault coverage up to 10% more efficient.
Encontrar falhas ou causas de problemas de desempenho em sistemas computacionais Web atuais é uma tarefa árdua que envolve muitas horas de análise de logs e métricas de sistemas. Para ajudar administradores nessa tarefa, diversos mecanismos de detecção de anomalia foram propostos visando analisar o comportamento do sistema mediante a coleta de um grande volume de informações estatísticas que demonstram o estado e o desempenho do sistema computacional. Uma das abordagens adotadas por esses mecanismo é o monitoramento por meio de correlações fortes identificadas no sistema. Nessa abordagem, a coleta desse grande número de dados gera inconvenientes associados à comunicação, armazenamento e, especialmente, com o processamento das informações coletadas. Apesar disso, poucos mecanismos de detecção de anomalias possuem uma estratégia para a seleção das informações estatísticas a serem coletadas, ou seja, para a seleção das métricas monitoradas. Este trabalho apresenta três filtros de seleção de métricas para mecanismos de detecção de anomalias baseados no monitoramento de correlações. Esses filtros foram baseados no conceito de correlação parcial, técnica que é capaz de fornecer informações não observáveis por métodos de correlações comuns. A validação desses filtros foi realizada sobre um cenário de aplicação Web, sendo que, para simular esse ambiente, nós utilizamos o TPC-W, um Benchmark de transações Web do tipo E-commerce. Os resultados obtidos em nossa avaliação mostram que um de nossos filtros permitiu a construção de uma rede de monitoramento com 8% menos métricas que filtros estado-da-arte, além de alcançar uma cobertura de falhas até 10% mais eficiente.

Esta tese de doutorado trata de algoritmos de detecção cooperativa aplicados ao problema de sensoriamento espectral em sistemas de rádios cognitivos. O problema de detecção cooperativa é abordado sob dois paradigmas distintos: detecção centralizada e distribuída. No primeiro caso, considera-se que o sistema conta com um centro de fusão responsável pela tomada de decisão no processo de detecção. Já no segundo caso, considera-se que os rádios cognitivos da rede trocam informações entre si e as decisões são tomadas localmente. No que concerne ao sensoriamento espectral centralizado, são estudados os casos em que os rádios cognitivos enviam apenas um bit de decisão para o centro de fusão (decisão do tipo hard) e também o caso em que o detector envia a própria estatística de teste ao centro de fusão (decisão do tipo soft). No âmbito de sensoriamento espectral cooperativo com detecção distribuída, são tratados três cenários diferentes. No primeiro, considera-se o caso em que os rádios cognitivos têm conhecimento a priori do sinal enviado pelo usuário primário do sistema e do canal entre eles e o usuário primário. No segundo caso, há conhecimento apenas do sinal enviado pelo usuário primário. Já no terceiro, os rádios cognitivos não dispõem de qualquer informação a priori do sinal enviado pelo usuário primário. Além do problema de detecção distribuída, a tese também apresenta um capítulo dedicado ao problema de estimação, diretamente associado ao de detecção. Esse último problema é abordado utilizando algoritmos derivados da teoria clássica de filtragem adaptativa.
This doctorate thesis deals with cooperative detection algorithms applied to the spectral sensing problem. The cooperative detection problem is approached under two different paradigms: centralized and distributed detection. In the first case, is considered that a fusion center responsible for detection decision is presented in the system. On the other hand, in the second case, is considered that the cognitive radios in the network exchange information among them. Concerning the centralized spectrum sensing system, the case in which the cognitive radios send only one decision bit (hard decision) to the fusion center and the case in which the detector send the statistic test (soft decision) are considered. Regarding the spectrum sensing system with distributed detection, the work analysis three different scenarios. In the first one, where the cognitive radios explore an a priori knowledge of the primary user signal and the channel between the primary user and the cognitive radio. In the second one, the cognitive radios use an a priori knowledge of only the primary user signal. And, in the las scenario, there is no a priori knowledge about the primary user signal. Besides the distributed detection problem, the thesis also presents a chapter dedicated to the estimation problem, which is directed related to the detection problem. This last issue is approached using adaptive algorithms derived from the classic adaptive filtering theory.

Este trabalho propõe um sistema fotovoltaico (PV) multifuncional conectado a um sistema elétrico trifásico a quatro fios e operando com estratégia dual de compensação ativa de potência. Além da injeção de potência ativa na rede elétrica, o sistema PV operara como um condicionador unificado de qualidade de energia (UPQC) suprimindo as correntes harmônicas da carga e compensando a potência reativa. Adicionalmente, tensões balanceadas, reguladas e livre de harmônicos são fornecidas para a carga. O sistema PV-UPQC é baseado em uma estratégia de compensação dual, na qual o conversor série opera como uma fonte de corrente senoidal, enquanto o conversor paralelo atua como uma fonte de tensão senoidal. Como resultado, nenhum comportamento transitório é observado na troca entre os modos de operação ilhado e interconectado do sistema PV-UPQC. Além disso, para contornar os problemas associados às mudanças bruscas de irradiação solar, uma malha de controle feed-forward atua na geração das referências de corrente do conversor série, de modo que se obtenha um rápido balanço de potência entre o sistema PV e a rede elétrica. Como resultado, as respostas dinâmicas tanto da corrente elétrica quanto da tensão do barramento CC são melhoradas. A análise detalhada envolvendo o fluxo de potência ativa dos inversores é realizada proporcionando um melhor entendimento sobre a operação do sistema PV-UPQC, bem como auxiliando no dimensionamento dos conversores de potência. Resultados de simulação e experimentais são apresentados para avaliar os desempenhos estáticos e dinâmicos do PV-UPQC conectado ao sistema elétrico de distribuição.
This work proposes a multifunctional single-stage three-phase four-wire grid-connected photovoltaic (PV) system operating with a dual active power-line compensating strategy. Besides injection of active power into the grid, the PV system operates as a unified power quality conditioner (UPQC), suppressing the load harmonic currents and compensating reactive power. Additionally, regulated, balanced and harmonic free output voltages are provided to the load. Since the PV-UPQC is based on a dual compensation strategy, the series converter operates as a sinusoidal current source, whereas the parallel converter operates as a sinusoidal voltage source. Thus, seamless transition can be achieved from the interconnected to the islanding operation modes, and vice-versa, without load voltage transients. Moreover, to overcome problems associated with sudden solar irradiation changes, fast power balance involving the PV array and the grid is obtained, since the feed-forward control loop acts on the generation of the series inverter current references. As a result, the dynamic responses of both inverter currents and dc-bus voltage are improved. Detailed analysis involving the active power flow through the inverters is performed allowing proper understanding of the PV-UPQC operation, as well as assisting to design of the power converters. Simulation and experimental results are presented to evaluate both dynamic and static performances of the PV-UPQC tied to the electrical distribution system.

Les travaux proposés dans cette thèse s’intéressent à la commande et la surveillance d’une classe particulière de systèmes de production : les systèmes manufacturiers à contraintes de temps de séjour. Nous supposons dans l'étude que les ressources ont déjà été affectées et que l'ordre des opérations est déjà fixé par le module de planification/ordonnancement. Les hypothèses de fonctionnement répétitif avec et sans postes d'assemblage sont adoptées. De manière assez classique pour ce type de problématique, le formalisme utilisé est celui des Réseaux de Petri P-temporels pour l'étude des instants de débuts et de fins des opérations.Une étude de la robustesse des ateliers manufacturiers à contraintes de temps a été développée. La robustesse est abordée avec et sans modification de la commande relative à la robustesse active et à la robustesse passive respectivement, face aux perturbations temporelles. Un algorithme de calcul d'une borne supérieure de la robustesse passive est présenté. De plus, trois stratégies de commande robuste face aux perturbations temporelles ont été développées.Par ailleurs, l’incertitude dans les systèmes de production manufacturière à été étudié. Notre contribution dans ce cadre porte sur l’intégration des résultats concernant la robustesse dans la génération de symptômes et la classification des comptes rendus associés aux différentes opérations en utilisant la logique floue.Partant d’un système commandé, nous avons présenté en détail une démarche à suivre pour la mise en œuvre d’un modèle de surveillance en se basant sur les chroniques et les arbres de défaillance flous. Cette démarche est appliquée à un atelier de production laitière
The works proposed in this thesis are interested in controlling and monitoring of a particular class of production system : manufacturing job-shops with time constraints. We suppose in the study that the resources are allocated and the operations order is fixed by the module of planning/scheduling. The assumptions of repetitive functioning mode with and without assembling tasks are adopted. For this type of problems, the formalism of P-time Petri nets is used in order to study the operations time constraints.A study of the robustness of the manufacturing workshop to time constraints, has been developed. The robustness is approached with and without control reaction qualified as active robustness and passive robustness respectively, towards time disturbances. A computing algorithm of the upper bound of the passive robustness is presented. In addition, three robust control strategies facing time disturbances were developed.Furthermore, uncertainty in manufacturing systems has been studied. Our contribution in this context is by integration of the analytical knowledge of the robustness in the filtering mechanism of sensors signals that are associated to operations, by using fuzzy logic.Starting from a controlled system, we have presented in detail, a method to be followed for the implementation of a monitoring model based on the chronicles and fuzzy fault tree analysis. This approach is applied to a milk production unit

Dans les systèmes de perception multicapteurs, un point central concerne le suivi d'objets multiples. Dans mes travaux de thèse, le capteur principal est un télémètre laser à balayage qui perçoit des cibles étendues. Le problème desuivi multi-objets se décompose généralement en plusieurs étapes (détection, association et suivi) réalisées de manière séquentielle ou conjointe. Mes travaux ont permis de proposer des alternatives à ces méthodes en adoptant une approche "track-before-detect" sur cibles distribuées qui permet d'éviter la succession des traitements en proposant un cadre global de résolution de ce problème d'estimation. Dans une première partie, nous proposons une méthode de détection travaillant directement en coordonnées naturelles (polaires) qui exploite les propriétés d'invariance géométrique des objets suivis. Cette solution est ensuite intégrée dans le cadre des approches JPDA et PHD de suivi multicibles résolues grâce aux méthodes de Monte-Carlo séquentielles. La seconde partie du manuscrit vise à s'affranchir du détecteur pour proposer une méthode dans laquelle le modèle d'objet est directement intégré au processus de suivi. C'est sur ce point clé que les avancées ont été les plus significatives permettant d'aboutir à une méthode conjointe de détection et de suivi. Un processus d'agrégation a été développé afin de permettre une formalisation des données qui évite tout prétraitement sous-optimal. Nous avons finalement proposé un formalisme général pour les systèmes multicapteurs (multilidar, centrale inertielle, GPS). D'un point de vue applicatif, ces travaux ont été validés dans le domaine du suivi de véhicules pour les systèmes d'aide à la conduite.

[EN] Fiber grating sensors and devices have demonstrated outstanding capabilities in both telecommunications and sensing areas, due to their well-known advantageous characteristics. Therefore, one of the most important motivations lies in the potential of customized fiber gratings to be suitably employed for improving the interrogation process of optical fiber sensors and systems. This Ph.D. dissertation is focused on the study, design, fabrication and performance evaluation of customized fiber Bragg gratings (FBGs) and long period gratings (LPGs) with the double aim to present novel sensing technologies and to enhance the response of existing sensing systems. In this context, a technique based on time-frequency domain analysis has been studied and applied to interrogate different kind of FBGs-based sensors. The distribution of the central wavelength along the sensing structures has been demonstrated, based on a combination of frequency scanning of the interrogating optical pulse and optical time-domain reflectometry (OTDR), allowing the detection of spot events with good performance in terms of measurand resolution. Moreover, different customized FBGs have been interrogated using a technology inspired on the operation principle of microwave photonics (MWP) filters, enabling the detection of spot events using radio-frequency (RF) devices with modest bandwidth. The sensing capability of these technological platforms has been fruitfully employed for implementing a large scale quasi-distributed sensor, based on an array of cascaded FBGs. The potentiality of LPGs as fiber optic sensors has also been investigated in a new fashion, exploiting the potentials of MWP filtering techniques. Besides, a novel approach for simultaneous measurements based on a half-coated LPG has been proposed and demonstrated. Finally, the feasibility of FBGs as selective wavelength filters has been exploited in sensing applications; an alternative approach to improve the response and performance of Brillouin distributed fiber sensors has been studied and validated via experiments. The performance of the reported sensing platforms have been analyzed and evaluated so as to characterize their impact on the fiber sensing field and to ultimately identify the use of the most suitable technology depending on the processing task to be carried out and on the final goal to reach.
[ES] Los sensores y dispositivos en fibra basados en redes de difracción han mostrado excepcionales capacidades en el ámbito de las telecomunicaciones y del sensado, gracias a sus excelentes propiedades. Entre las motivaciones más estimulantes destaca la posibilidad de fabricar redes de difracción ad-hoc para implementar y/o mejorar las prestaciones de los sensores fotónicos. Esta tesis doctoral se ha enfocado en el estudio, diseño, fabricación y evaluación de las prestaciones de redes de difracción de Bragg (FBGs) y de redes de difracción de periodo largo (LPGs) personalizadas con el fin de desarrollar nuevas plataformas de detección y a la vez mejorar la respuesta y las prestaciones de los sensores fotónicos ya existentes. En este contexto, una técnica basada en el análisis tiempofrecuencia se ha estudiado e implementado para la interrogación de sensores en fibra basados en varios tipos y modelos de FBGs. Se ha analizado la distribución de la longitud de onda central a lo largo de la estructura de sensado, gracias a una metodología que conlleva el escaneo en frecuencia del pulso óptico incidente y la técnica conocida como reflectometria óptica en el dominio del tiempo (OTDR). De esta manera se ha llevado a cabo la detección de eventos puntuales, alcanzando muy buenas prestaciones en términos de resolución de la magnitud a medir. Además, se han interrogado varias FBGs a través de una técnica basada en el principio de operación de los filtros de fotónica de microondas (MWP), logrando así la detección de eventos puntuales usando dispositivos de radio-frecuencia (RF) caracterizados por un moderado ancho de banda. La capacidad de sensado de estas plataformas tecnológicas ha sido aprovechada para la realización de un sensor quasi-distribuido de gran alcance, formado por una estructura en cascada de muchas FBGs. Por otro lado, se han puesto a prueba las capacidades de las LPGs como sensores ópticos según un enfoque novedoso; para ello se han aprovechados las potencialidades de los filtros de MWP. Asimismo, se ha estudiado y demostrado un nuevo método para medidas simultáneas de dos parámetros, basado en una LPG parcialmente recubierta por una película polimérica. Finalmente, se ha explotado la viabilidad de las FBGs en cuanto al filtrado selectivo en longitud de onda para aplicaciones de sensado; para ello se ha propuesto un sistema alternativo para la mejora de la respuesta y de las prestaciones de sensores ópticos distribuidos basados en el scattering de Brillouin. En conclusión, se han analizado y evaluado las prestaciones de las plataformas de sensado propuestas para caracterizar su impacto en el ámbito de los sistemas de detección por fibra y además identificar el uso de la tecnología más adecuada dependiendo de la tarea a desarrollar y del objetivo a alcanzar.
[CAT] Els sensors i dispositius en fibra basats en xarxes de difracció han mostrat excepcionals capacitats en l'àmbit de les telecomunicacions i del sensat, gràcies a les seus excel¿lents propietats. Entre les motivacions més estimulants destaca la possibilitat de fabricar xarxes de difracció ad-hoc per a implementar i/o millorar les prestacions de sensors fotònics. Esta tesi doctoral s'ha enfocat en l'estudi, disseny, fabricació i avaluació de les prestacions de xarxes de difracció de Bragg (FBGs) i de xarxes de difracció de període llarg (LPGs) personalitzades per tal de desenvolupar noves plataformes de detecció i al mateix temps millorar la resposta i les prestacions dels sensors fotònics ja existents. En este context, una tècnica basada en l'anàlisi temps-freqüència s'ha estudiat i implementat per a la interrogació de sensors en fibra basats en diversos tipus i models de FBGs. S'ha analitzat la distribució de la longitud d'ona central al llarg de l'estructura de sensat, gràcies a una metodologia que comporta l'escaneig en freqüència del pols òptic incident i la tècnica coneguda com reflectometria òptica en el domini del temps (OTDR). D'esta manera s'ha dut a terme la detecció d'esdeveniments puntuals, aconseguint molt bones prestacions en termes de resolució de la magnitud a mesurar. A més, s'han interrogat diverses FBGs a través d'una tècnica basada en el principi d'operació dels filtres de fotònica de microones (MWP), aconseguint així la detecció d'esdeveniments puntuals utilitzant dispositius de ràdio-freqüència (RF) caracteritzats per un moderat ample de banda. La capacitat de sensat d'aquestes plataformes tecnològiques ha sigut aprofitada per a la realització d'un sensor quasi-distribuït a llarga escala, format per una estructura en cascada de moltes FBGs. D'altra banda, s'han posat a prova les capacitats de les LPGs com a sensors òptics segons un enfocament nou; per a això s'han aprofitat les potencialitats dels filtres de MWP. Així mateix, s'ha estudiat i demostrat un nou mètode per a mesures simultànies de dos paràmetres, basat en una LPG parcialment recoberta per una pel¿lícula polimèrica. Finalment, s'ha explotat la viabilitat de les FBGs pel que fa al filtrat selectiu en longitud d'ona per a aplicacions de sensat; per això s'ha proposat un sistema alternatiu per a la millora de la resposta i de les prestacions de sensors òptics distribuïts basats en el scattering de Brillouin. S'han analitzat i avaluat les prestacions de les plataformes de sensat propostes per a caracteritzar el seu impacte en l'àmbit dels sistemes de detecció per fibra i a més identificar l'ús de la tecnologia més adequada depenent de la tasca a desenvolupar i de l'objectiu a assolir.
Ricchiuti, AL. (2016). Design and fabrication of customized fiber gratings to improve the interrogation of optical fiber sensors [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/66343
TESIS
Premiado

Ce travail de recherche est dédié à l’élaboration d’une stratégie de navigation autonome qui comprend la génération d’une trajectoire optimale en évitant des obstacles, la détection de l’objet d’intérêt spécifique (i.e. une fenêtre) et puis l’exécution de la manoeuvre postérieure à approcher la fenêtre et enfin accéder à l’intérieur du bâtiment. Le véhicule est navigué par un système de vision et une combinaison de capteurs inertiels et d’altitude, ce qui réalise une localisation relative du quadri-rotor par rapport à son environment. Une méthode de planification de trajectoire basée sur Model Predictive Control (MPC), qui utilise les informations fournies par le GPS et le capteur visuel, a été conçue pour générer une trajectoire optimale en temps réel avec des capacités d’évitement de collision, qui commence à partir d’un point initial donné par l’utilisateur et guide le véhicule pour atteindre le point final à l’extérieur du bâtiment de la cible. Dans le but de détecter et de localiser l’objet d’intérêt, deux stratégies de détection d’objet basées sur la vision sont proposées et sont respectivement appliquées dans le système de stéréo vision et le système de vision en utilisant la Kinect. Après l’estimation du modèle de la fenêtre cible, un cadre d’estimation de mouvement est conçu pour estimer ego-mouvement du véhicule à partir des images fournies par le capteur visuel. Il y a eu deux versions des cadres d’estimation de mouvement pour les deux systèmes de vision. Une plate-forme expérimentale de quad-rotor est développée. Pour l’estimation de la dynamique de translation du véhicule, un filtre de Kalman est mis en œuvre pour combiner les capteurs d’imagerie, inertiels et d’altitude. Un système de détection et de contrôle hiérarchique est conçu pour effectuer la navigation et le contrôle de l’hélicoptère quadri-rotor, ce qui permet au véhicule d’estimer l’état sans marques artificielles ou d’autres systèmes de positionnement externes
This research work is dedicated to the development of an autonomous navigation strategy which includes generating an optimal trajectory with obstacles avoiding capabilities, detecting specific object of interest (i.e. a window) and then conducting the subsequent maneuver to approach the window and finally access into the building. The vehicle is navigated by a vision system and a combination of inertial and altitude sensors, which achieve a relative localization of the quad-rotor with respect to its surrounding environment. A MPC-based path planning method using the information provided by the GPS and the visual sensor has been developed to generate an optimal real-time trajectory with collision avoidance capabilities, which starts from an initial point given by the user and guides the vehicle to achieve the final point outside the target building. With the aim of detecting and locating the object of interest, two different vision-based object detection strategies are proposed and are applied respectively in the stereo vision system and the vision system using the Kinect. After estimating the target window model, a motion estimation framework is developed to estimate the vehicle’s ego-motion from the images provided by the visual sensor. There have been two versions of the motion estimation frameworks for both vision systems. A quad-rotor experimental platform is developed. For estimating the translational dynamic of the vehicle, a Kalman filter is implemented to combine the imaging, inertial and altitude sensors. A hierarchical sensing and control system is designed to perform the navigation and control of the quad-rotor helicopter, which allows the vehicle to estimate the state without artificial marks or other external positioning systems

"This dissertation takes a filtering oriented point of view and systematically addresses modeling, filtering performance analysis, and filter design issues over sensor networks with network induced constraints. The overall aim of this dissertation is to expand knowledge about theory and applications of sensor networks. More specifically, rigorous and systematic theoretical frameworks of distributed H1 filtering for estimating an unavailable state signal through noisy measurement and a disturbed plant over sensor networks are established. Furthermore, new filtering concepts, filtering theory, filter design methods, and filtering algorithms are proposed to deal with various network-induced constraints."

碩士
國立中興大學
電機工程學系所
101
In the wireless sensor network, several localization algorithms have been proposed for indoor positioning systems. However, the computational complexity of these schemes is high, which may not be suitable to be implemented in sensor nodes. For example, the limited sensor capabilities lead to performing the particle filtering with a very small set of samples, which results in high positioning errors. Hence, a novel sampling scheme may be required to improve estimation accuracy for the particle filter method. In this thesis, the concept of support vector regression (SVR) is conducted to suppress the estimation error, which enhances the reliability of the positioning system. Accordingly, we propose a Kernel-Based Particle Filtering (KBPF) algorithm, which consists of the following three steps: (1) Initial SVR Estimation; (2) Kernel-based Re-weighting; and (3) Estimation Refinement. The experimental results show that the proposed scheme can achieve good indoor positioning accuracy with a small number of samples and the performance of the proposed KBPF system using three beacons is comparable with that of the KLF system using four beacons.

碩士
國立中興大學
電機工程學系所
99
This thesis presents distributed methods for target tracking in wireless sensor networks. We consider flat network and cluster-based network topologies for target tracking and provide leader-based information processing to track the target. The method with a flat network uses a timer to create clusters dynamically and the method with a cluster-based network uses the Clustering Algorithm via Waiting Timer (CAWT) to create the cluster model. Particle filtering is applied to estimate the location of the target, and then Covariance Intersection algorithm is used to make data fusion with local estimates. Finally, we compare the estimation accuracy with the two different network topologies.

碩士
國立屏東商業技術學院
資訊管理系
93
With the popularity of Internet, the variation of the society is amazing. Internet facilitates the information transmission and globalization. However, some risks emerge immediately. The DDoS attack causes the services of internet in danger. The reason why DDoS hard to deal with is because the attacker source is difficult to track. It uses the spoofed packets to confuse the victim making the real attack source hard to find, and the attacker behind the scenes can using the legitimate or illegitimate traffic to crash the victim’s host. In this paper, we propose a packet filter system to determine the truthfulness of the incoming packets to the host in order to defense the DDoS attack.

Networked systems are systems in which data transmissions among system components are completed through communication networks. As one fundamental issue, distributed filtering of networked systems has gained growing attention in the fields of control and signal processing, and its objective is to estimate system states using noisy output measurements. Thus, distributed filtering for networked systems has found a wide range of applications in areas such as health care monitoring, security detecting, industrial process automation, and environmental monitoring. A defining feature of distributed filtering is that connections among nodes of a distributed system is completed through communication networks rather than point-to-point cables. Compared with traditional point-to-point connections, communication networks can bring several advantages, e.g. low cost, high reliability, ease of maintenance and reconfiguration, and flexible system architecture, especially for distributed systems, where sensors, actuators and controllers are spatially distributed far away from each other. However, communication networks may have some challenging issues, which make the distributed filtering problem complicated. First, because of limited bandwidth, network-induced delays and packet dropouts are unavoidable, which usually deteriorate the system performance. Second, due to the dynamic characteristics of networks, the topology of a distributed system is not fixed, which is difficult to be dealt with when current system states are estimated. Third, as a result of limited network resources, the network traffic may be congested if all the sampling periods of sensor nodes are very small, which is undesired in the distributed filtering. Therefore, it is significant to address the problem of distributed filtering by taking the above challenging issues into account, which motivates this study. This thesis is concerned with distributed filtering for networked systems. Several results have been reported on the effects of network-induced constraints including delays and packet dropouts, dynamic topologies, and network resource utilizations, on the performance of distributed H∞ filtering error system. More specifically, the emphasis is placed on the following four aspects: • Event-triggered distributed H∞ filtering. In order to save limited network resources, a distributed event-triggered transmission scheme is introduced to choose those necessary data packets to be transmitted among neighboring filters. Under the event-triggered transmission scheme, the resultant filtering error system is modeled as a time-delay system. By employing Lyapunov- Krasovskii functional method, both event-triggered parameters and distributed H∞ filter parameters can be designed in terms of linear matrix inequalities. • Distributed H∞ filtering for continuous-time systems over sensor networks with dynamical topologies. The distributed H∞ filtering for continuous-time systems over sensor networks is investigated. On the one hand, the effects of data packet dropouts on the distributed H∞ filtering are investigated. On the other hand, a novel distributed event-triggered scheme is introduced to transmit those necessary data packets in the case that the topology of the sensor network is dynamic. Moreover, algorithms are also given to design suitable distributed H∞ filters. • Event-triggered distributed H∞ filtering for multi-rate systems subject to data packet dropouts. For multi-rate distributed systems, event-triggered distributed H∞ filtering is studied by taking packet dropouts into account. By applying a lifting technique, the resultant multi-rate distributed filtering error system is transformed into a high dimensional single-rate system. As a result, novel sufficient conditions are established such that the distributed filtering error system is mean-square exponentially stable with a desired H∞ attenuation level. Moreover, an iterative algorithm is presented for the design of both distributed filters and event-triggered parameters. • Event-triggered distributed H∞ filtering for sensor networks under Round-Robin scheduling. Event-triggered distributed H∞ filtering for sensor networks is investigated under Round-Robin scheduling. Each sensor is equipped with a few samplers, and the scheduling of signals from samplers to filters is ruled by the classical Round-Robin protocol. In order to save precious network resources, a novel event-triggered transmission scheme is presented, where network-induced delays are taken into account. Moreover, the resultant filtering error system is modeled as a switched system with multiple time-varying delays. Switched system approaches are thus employed to design distributed H∞ filters based on a set of LMIs. Several simulation results are provided to validate the proposed methods.

Thesis (Ph. D.)--University of Notre Dame, 2008.
Thesis directed by Peter H. Bauer for the Department of Electrical Engineering. "August 2008." Includes bibliographical references (leaves 138-149).

With the increasing availability of large datasets machine learning techniques are becoming an increasingly attractive alternative to expert-designed approaches to solving complex problems in domains where data is abundant. In this thesis we introduce several models for large sparse discrete datasets. Our approach, which is based on probabilistic models that use distributed representations to alleviate the effects of data sparsity, is applied to statistical language modelling and collaborative filtering. We introduce three probabilistic language models that represent words using learned real-valued vectors. Two of the models are based on the Restricted Boltzmann Machine (RBM) architecture while the third one is a simple deterministic model. We show that the deterministic model outperforms the widely used n-gram models and learns sensible word representations. To reduce the time complexity of training and making predictions with the deterministic model, we introduce a hierarchical version of the model, that can be exponentially faster. The speedup is achieved by structuring the vocabulary as a tree over words and taking advantage of this structure. We propose a simple feature-based algorithm for automatic construction of trees over words from data and show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models. We then turn our attention to collaborative filtering and show how RBM models can be used to model the distribution of sparse high-dimensional user rating vectors efficiently, presenting inference and learning algorithms that scale linearly in the number of observed ratings. We also introduce the Probabilistic Matrix Factorization model which is based on the probabilistic formulation of the low-rank matrix approximation problem for partially observed matrices. The two models are then extended to allow conditioning on the identities of the rated items whether or not the actual rating values are known. Our results on the Netflix Prize dataset show that both RBM and PMF models outperform online SVD models.

A solution to the vehicle state estimation problem is given using the Kalman filtering and the Particle filtering theories. Vehicle states are necessary for an active or a semi-active suspension control system, which is intended to enhance ride comfort, road handling and stability of the vehicle. Due to a lack of information on road disturbances, conventional estimation techniques fail to provide accurate estimates of all the required states. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called a supervisory layer, operates on the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter’s last updated estimate. Then, a resampling stage is implemented to collect the particles with higher probability. The effectiveness of the SKF is demonstrated by comparing its estimation results with that of the Kalman filter and the particle filter when a test vehicle is passing over a bump. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, without any direct measurement of the road disturbance inputs. Once the vehicle states are provided, a suspension control law, the Skyhook strategy, processes the current states and adjusts the damping forces accordingly to provide a better and safer ride for the vehicle passengers. This thesis presents a novel systematic and practical methodology for the design and implementation of the Skyhook control strategy for vehicle’s semi-active suspension systems. Typically, the semi-active control strategies (including the Skyhook strategy) have switching natures. This makes the design process difficult and highly dependent on extensive trial and error. The proposed methodology maps the discontinuous control system model to a continuous linear region, where all the time/frequency design techniques, established in the conventional control system theory, can be applied. If the semiactive control law is designed to satisfy ride and stability requirements, an inverse mapping offers the ultimate control law. The effectiveness of the proposed methodology in the design of a semi-active suspension control system for a Cadillac SRX 2005 is demonstrated by real-time road tests. The road tests results verify that the use of the newly developed systematic design methodology reduces the required time and effort in real industrial problems.

碩士
長庚大學
資訊管理研究所
94
Since the Internet was invented, Email system has become one of the most frequently used communication platforms. However, a large amount of users are troubled with overflowing junk mails, because they have to spend a lot of efforts in dealing with those digital junks. The worst case happens when sometimes important mails are mistakenly deleted. This research aims at designing a distributed Email system on a peer-to-peer network. A user can receive emails based on customized priorities in his own contact list. Even more he can only accept emails from certain designated senders. With this system, therefore, users can eliminate the chance of getting commercial or junk mails, as they can arbitrarily choose the source addresses. In order to analyze this system’s performance, this study conducts simulations to observe the influence of the number of nodes on communication time in receiving emails and the average storage cost of network nodes. The results show that the communication time is sublinearly to the number of nodes, as described in the Chord publication. On the other hand, as network nodes are more evenly distributed over the Chord ring, the variance of storage costs on each node tends to be smaller.

碩士
國立清華大學
資訊工程學系
103
User-generated information is greatly increasing because of the growing of Internet. Recommender systems which make use of users’ information are getting more and more attention. The recommender system can be simply separate as content-based and collaborative filtering. To deal with large scale of data, the parallelism is more and more necessary for recommender systems. However, when we implement the parallelism, the data loss which is because of data transmission is also an important issue to be solved. If the problem of data loss is too serious, the accuracy of whole system might greatly influence. The distributed storage system might be a solution of this problem. With a computable storage environment, the parallelism of recommender system can be implemented on it. Moreover, the requirement of repairing data can also be satisfied. In this thesis, we propose a framework that combines movie recommender system with a proxy-based distribution storage environment. In our proxy-based framework, whole movie recommender system can process in parallel. The main contribution of our proposed framework is that we propose a clustering algorithm to improve our framework. We use simple but efficient method to scatter data to reduce bandwidth of data transmission and improve the accuracy of whole framework. On the other hand, when the data is lost, the data can be repaired completely. In distribution storage, the method we adopt is NCCloud which implements FMSR codes. In our experiments, we compare proposed clustering algorithm with uniform distribution in root mean square error (RMSE), bandwidth, and execution time. We also show the difference in speedup of single node and our proposed framework. Finally, the discussion of difference whether data is repaired is present. Keywords: Distributed storage system, Recommender system, Data distribution, Fault tolerance

Toshio M. Chin, William C. Karl, Alan S. Willsky.
Caption title.
Includes bibliographical references.
Supported by the Air Force Office of Scientific Research. F49620-92-J-002 Supported by the Office of Naval Research. N00014-91-J-1120 N00014-91-J-1004 Supported by the Army Research Office. DAAL03-92-G-0115

碩士
國立臺灣大學
資訊管理學研究所
96
Distributed Denial-of-Service (DDoS) attacks have become an impending threat toward today’s Internet. During DDoS attacks, numerous malicious packets occupy a victim server and lead to the difficulty of the legitimate user’s access. Even if the filtering thwarts DDoS attacks, no legitimate users can escape the collateral damage. In this thesis, we model the DDoS attack-defense scenario as a two-level mathematical programming problem. In the inner problem, a defender tries to allocate the limited defense resources for the maximization of the legitimate traffic. In the outer problem, a DDoS attacker tries to allocate the limited attack resources in order to minimize the legitimate traffic. A Lagrangean relaxation-based algorithm is proposed to solve the inner problem, and a subgradient-based algorithm is proposed to solve the outer problem.

碩士
國立交通大學
應用數學系
91
With the rapid development of Internet, the Internet is becoming more and more complicated. The security is one of the most important issues today in Internet. Under the issues of security, distributed denial of service attack is a critical problem and has not been solved completely yet. To solve this problem, the administrators traditionally use the detecting and filtering approach to mitigate the damage caused by distributed DoS attacks. However, only few administrators have the expertise to filter out garbage traffic even if the occurrence of distributed DoS attacks are detected. Therefore, a knowledge-based model is proposed to defend distributed DoS attacks. In this model, the prior knowledge such as access control information is acquired from the ordinary network information by our proposed access control information maintenance module firstly. These access control information are then used to be the filtering policy of the defending system when the attack traffic is coming. The simulation study using Berkeley Network Simulator is also proposed to evaluate our defending system. Finally, the evaluation study shows that most of the frequent users can be protected in the defending system.

Networked control systems (NCSs) present many advantages such as easy installation and maintenance, flexible layouts and structures of components, and efficient allocation and distribution of resources. Consequently, they find potential applications in a variety of emerging industrial systems including multi-agent systems, power grids, tele-operations and cyber-physical systems. The study of NCSs with nonlinear dynamics (i.e., nonlinear NCSs) is a very significant yet challenging topic, and it not only widens application areas of NCSs in practice, but also extends the theoretical framework of NCSs with linear dynamics (i.e., linear NCSs). Numerous issues are required to be resolved towards a fully-fledged theory of industrial nonlinear NCS design. In this dissertation, three important problems of nonlinear NCSs are investigated: The robust filtering problem, the robust model predictive control (MPC) problem and the robust distributed MPC problem of large-scale nonlinear systems. In the robust filtering problem of nonlinear NCSs, the nonlinear system model is subject to uncertainties and external disturbances, and the measurements suffer from time delays governed by a Markov process. Utilizing the Lyapunov theory, the algebraic Hamilton-Jacobi inequality (HJI)-based sufficient conditions are established for designing the H_infty nonlinear filter. Moreover, the developed results are specialized for a special type of nonlinear systems, by presenting the HJI in terms of matrix inequalities. For the robust MPC problem of NCSs, three aspects are considered. Firstly, to reduce the computation and communication load, the networked MPC scheme with an efficient transmission and compensation strategy is proposed, for constrained nonlinear NCSs with disturbances and two-channel packet dropouts. A novel Lyapunov function is constructed to ensure the input-to-state practical stability (ISpS) of the closed-loop system. Secondly, to improve robustness, a networked min-max MPC scheme are developed, for constrained nonlinear NCSs subject to external disturbances, input and state constraints, and network-induced constraints. The ISpS of the resulting nonlinear NCS is established by constructing a new Lyapunov function. Finally, to deal with the issue of unavailability of system state, a robust output feedback MPC scheme is designed for constrained linear systems subject to periodical measurement losses and external disturbances. The rigorous feasibility and stability conditions are established. For the robust distributed MPC problem of large-scale nonlinear systems, three steps are taken to conduct the studies. In the first step, the issue of external disturbances is addressed. A robustness constraint is proposed to handle the external disturbances, based on which a novel robust distributed MPC algorithm is designed. The conditions for guaranteeing feasibility and stability are established, respectively. In the second step, the issue of communication delays are dealt with. By designing the waiting mechanism, a distributed MPC scheme is proposed, and the feasibility and stability conditions are established. In the third step, the robust distributed MPC problem for large-scale nonlinear systems subject to control input constraints, communication delays and external disturbances are studied. A dual-mode robust distributed MPC strategy is designed to deal with the communication delays and the external disturbances simultaneously, and the feasibility and the stability conditions are developed, accordingly.
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