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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Upgrading or replacing production systems is always a very resource-consuming task, in particular if the systems being replaced are quite specialized, such as those serving any Flight Test Ground Station. In the recent past a large number of Ground Station systems were based in Digital’s VAX/VMS architecture. The computer industry then expanded very fast and by 1990 realtime PCM data processing systems totally dependent on hardware and software designed for IBM-PC compatible micro-computers were becoming available. A complete system replacement in a typical Ground Station can take from one to several years to become a reality. It depends on how complex the original system is, how complex the resulting system needs to be, how much resources are available to support the operation, how soon the organization needs it, etc. This paper intends to review the main concerns encountered during the replacement of a typical VAX/VMS-based by an Intel-Windows NT-based Ground Station. It covers the transition from original requirements to totally new requirements, from mini-computers to micro-computers, from DMA to high-speed LAN data transfers, while conserving some key architectural features. This 8-month development effort will expand EMBRAER’s capability in acquiring, processing and archiving PCM data in the next few years at a lower cost, while preserving compatibility with old legacy flight test data.

Modern health self-monitoring devices and applications, such as Fitbit and MyFitnessPal, empower users to take concrete actions and set fitness and lifestyle goals based on their recorded trends and statistics. Predicting such trends is beneficial in the road of achieving long-time targets, as the individuals can adjust their diets and habits at any point to guarantee success. The design and implementation of such a system, which also respects user privacy, is the main objective of our work.This application is modelled as a time-series forecasting problem. Given the historical data of users, we aim to predict their eating and lifestyle habits in real-time. We apply the federated learning paradigm to our use-case be- cause of the highly-distributed nature of our data and the privacy concerns of such sensitive recorded information. However, federated learning from het- erogeneous sequences of data can be challenging, as even state-of-the-art ma- chine learning techniques for time-series forecasting can encounter difficulties when learning from very irregular data sequences. Specifically, in the pro- posed healthcare scenario, the machine learning algorithms might fail to cater to users with unique dietary patterns.In this work, we implement a two-step streaming clustering mechanism and group clients that exhibit similar eating and fitness behaviours. The con- ducted experiments prove that learning federatively in this context can achieve very high prediction accuracy, as our predictions are no more than 0.025% far from the ground truth value with respect to the range of each feature. Training separate models for each group of users is shown to be beneficial, especially in terms of the training time, but it is highly dependent on the parameters used for the models and the training process. Our experiments conclude that the configuration used for the general federated model cannot be applied to the clusters of data. However, a decrease in prediction error of more than 45% can be achieved, given the parameters are optimized for each case.Lastly, this work tackles the problem of data privacy by applying state-of- the-art differential privacy techniques. Our empirical study shows that noising the gradients sent to the server is unsuitable for small datasets and cancels out the benefits obtained by prior users’ clustering. On the other hand, noising the training data achieves remarkable results, obtaining a differential privacy level corresponding to an epsilon value of 0.1 with an increase in the observed mean absolute error by a factor of only 0.21. / Moderna apparater och applikationer för självövervakning av hälsa, som Fitbit och MyFitnessPal, ger användarna möjlighet att vidta konkreta åtgärder och sätta fitness- och livsstilsmål baserat på deras dokumenterade trender och statistik. Att förutsäga sådana trender är fördelaktigt för att uppnå långtidsmål, eftersom individerna kan anpassa sina dieter och vanor när som helst för att garantera framgång.Utformningen och implementeringen av ett sådant system, som dessutom respekterar användarnas integritet, är huvudmålet för vårt arbete. Denna appli- kation är modellerad som ett tidsserieprognosproblem. Med avseende på an- vändarnas historiska data är målet att förutsäga deras matvanor och livsstilsva- nor i realtid. Vi tillämpar det federerade inlärningsparadigmet på vårt använd- ningsfall på grund av den mycket distribuerade karaktären av vår data och in- tegritetsproblemen för sådan känslig bokförd information. Federerade lärande från heterogena datasekvenser kan emellertid vara utmanande, eftersom även de modernaste maskininlärningstekniker för tidsserieprognoser kan stöta på svårigheter när de lär sig från mycket oregelbundna datasekvenser. Specifikt i det föreslagna sjukvårdsscenariot kan maskininlärningsalgoritmerna misslyc- kas med att förse användare med unika dietmönster.I detta arbete implementerar vi en tvåstegsströmmande klustermekanism och grupperar användare som uppvisar liknande ät- och fitnessbeteenden. De genomförda experimenten visar att federerade lärande i detta sammanhang kan uppnå mycket hög nogrannhet i förutsägelse, eftersom våra förutsägelser in- te är mer än 0,025% ifrån det sanna värdet med avseende på intervallet för varje funktion. Träning av separata modeller för varje grupp användare visar sig vara fördelaktigt, särskilt gällande träningstiden, men det är mycket be- roende av parametrarna som används för modellerna och träningsprocessen. Våra experiment drar slutsatsen att konfigurationen som används för den all- männa federerade modellen inte kan tillämpas på dataklusterna. Dock kan en minskning av förutsägelsefel på mer än 45% uppnås, givet att parametrarna är optimerade för varje fall.Slutligen hanteras problemet med datasekretess genom att tillämpa bästa tillgängliga differentiell integritetsteknik. Vår empiriska studie visar att adde- ra brus till gradienter som skickas till servern är olämpliga för liten data och avbryter fördelarna med tidigare användares kluster. Däremot, genom att ad- dera brus till träningsdata uppnås anmärkningsvärda resultat. En differentierad integritetsnivå motsvarande ett epsilonvärde på 0,1 med en ökning av det ob- serverade genomsnittliga absoluta felet med en faktor på endast 0,21 erhölls.

A k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time.

The objective of this thesis is to design a relative navigation and guidance system for unmanned aerial vehicles (UAVs) for vision-based control applications. The vision-based navigation, guidance and control has been one of the most focused on research topics for the automation of UAVs. This is because in nature, birds and insects use vision as the exclusive sensor for object detection and navigation. In particular, this thesis studies the monocular vision-based navigation and guidance. Since 2-D vision-based measurements are nonlinear with respect to the 3-D relative states, an extended Kalman filter (EKF) is applied in the navigation system design. The EKF-based navigation system is integrated with a real-time image processing algorithm and is tested in simulations and flight tests. The first closed-loop vision-based formation flight has been achieved. In addition, vision-based 3-D terrain recovery was performed in simulations. A vision-based obstacle avoidance problem is specially addressed in this thesis. A navigation and guidance system is designed for a UAV to achieve a mission of waypoint tracking while avoiding unforeseen stationary obstacles by using vision information. A 3-D collision criterion is established by using a collision-cone approach. A minimum-effort guidance (MEG) law is applied for a guidance design, and it is shown that the control effort can be reduced by using the MEG-based guidance instead of a conventional guidance law. The system is evaluated in a 6 DoF flight simulation and also in a flight test. For monocular vision-based control problems, vision-based estimation performance highly depends on the relative motion of the vehicle with respect to the target. Therefore, this thesis aims to derive an optimal guidance law to achieve a given mission under the condition of using the EKF-based relative navigation. Stochastic optimization is formulated to minimize the expected cost including the guidance error and the control effort. A suboptimal guidance law is derived based on an idea of the one-step-ahead (OSA) optimization. Simulation results show that the suggested guidance law significantly improves the guidance performance. Furthermore, the OSA optimization is generalized as the n-step-ahead optimization for an arbitrary number of n, and their optimality and computational cost are investigated.

A k nearest neighbor query q retrieves k objects that lie closest to the query point q among a given set of objects P. With the availability of inexpensive location aware mobile devices, the continuous monitoring of such queries has gained lot of attention and many methods have been proposed for continuously monitoring the kNNs in highly dynamic environment. Multiple continuous queries require real-time results and both the objects and queries issue frequent location updates. Most popular spatial index, R-tree, is not suitable for continuous monitoring of these queries due to its inefficiency in handling frequent updates. Recently, the interest of database community has been shifting towards using grid-based index for continuous queries due to its simplicity and efficient update handling. For kNN queries, the order in which cells of the grid are accessed is very important. In this research, we present two efficient and effective grid access methods, CircularTrip and ArcTrip, that ensure that the number of cells visited for any continuous kNN query is minimum. Our extensive experimental study demonstrates that CircularTrip-based continuous kNN algorithm outperforms existing approaches in terms of both efficiency and space requirement. Moreover, we show that CircularTrip and ArcTrip can be used for many other variants of nearest neighbor queries like constrained nearest neighbor queries, farthest neighbor queries and (k + m)-NN queries. All the algorithms presented for these queries preserve the properties that they visit minimum number of cells for each query and the space requirement is low. Our proposed techniques are flexible and efficient and can be used to answer any query that is hybrid of above mentioned queries. For example, our algorithms can easily be used to efficiently monitor a (k + m) farthest neighbor query in a constrained region with the flexibility that the spatial conditions that constrain the region can be changed by the user at any time.

Thesis (M.S.) -- Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

CAPES / Este trabalho tem por objetivo o desenvolvimento de uma arquitetura de baixo custo para construção de unidades de medição fasorial sincronizada (PMU). O dispositivo prevê conexão com a baixa tensão da rede elétrica, de forma que, instalada neste ponto do sistema permita o monitoramento da rede de transmissão e distribuição. Os desenvolvimentos deste projeto contemplam uma arquitetura completa, com módulo de instrumentação para uso na baixa tens˜ao da rede, módulo GPS para fornecer o sinal de sincronismo e etiqueta de tempo das medidas, unidade de processamento com sistema de aquisição, estimação de fasores e formatação dos dados de acordo com a norma e, por fim, módulo de comunicação para transmissão dos dados. Para o desenvolvimento e avaliação do desempenho da arquitetura, desenvolveu-se um conjunto de aplicativos em ambiente LabVIEW com funcionalidades específicas que permitem analisar o comportamento das medidas e identificar as fontes de erro da PMU, além de aplicar todos os testes previstos pela norma IEEE C37.118.1. O primeiro aplicativo, útil para o desenvolvimento da instrumentação, consiste em um gerador de funções integrado com osciloscópio, que permite a geração e aquisição de sinais de forma sincronizada, além da manipulação das amostras. O segundo e principal deles, é a plataforma de testes capaz de gerar todos os ensaios previstos pela norma, permitindo também armazenar os dados ou fazer a análise das medidas em tempo real. Por fim, um terceiro aplicativo foi desenvolvido para avaliar os resultados dos testes e gerar curvas de ajuste para calibração da PMU. Os resultados contemplam todos os testes previstos pela norma e um teste adicional que avalia o impacto de ruído. Além disso, através de dois protótipos conectados à instalação elétrica de consumidores de um mesmo circuito de distribuição, obteve-se registros de monitoramento que permitiram a identificação das cargas no consumidor, análise de qualidade de energia, além da detecção de eventos a nível de distribuição e transmissão. / This work presents a low cost architecture for development of synchronized phasor measurement units (PMU). The device is intended to be connected in the low voltage grid, which allows the monitoring of transmission and distribution networks. Developments of this project include a complete PMU, with instrumentation module for use in low voltage network, GPS module to provide the sync signal and time stamp for the measures, processing unit with the acquisition system, phasor estimation and formatting data according to the standard and finally, communication module for data transmission. For the development and evaluation of the performance of this PMU, it was developed a set of applications in LabVIEW environment with specific features that let analyze the behavior of the measures and identify the sources of error of the PMU, as well as to apply all the tests proposed by the standard. The first application, useful for the development of instrumentation, consists of a function generator integrated with an oscilloscope, which allows the generation and acquisition of signals synchronously, in addition to the handling of samples. The second and main, is the test platform, with capabality of generating all tests provided by the synchronized phasor measurement standard IEEE C37.118.1, allowing store data or make the analysis of the measurements in real time. Finally, a third application was developed to evaluate the results of the tests and generate calibration curves to adjust the PMU. The results include all the tests proposed by synchrophasors standard and an additional test that evaluates the impact of noise. Moreover, through two prototypes connected to the electrical installation of consumers in same distribution circuit, it was obtained monitoring records that allowed the identification of loads in consumer and power quality analysis, beyond the event detection at the distribution and transmission levels.

CNPq, CAPES / O presente trabalho inclui a revisão das normas de qualidade de energia elétrica, a fim de normatizar o desenvolvimento do projeto seguindo normas nacionais e internacionais; a simulação de algoritmos como CFA e FFT, a fim de verificar a viabilidade de seu uso, bem como as limitações associadas ao processamento de formas de onda fortemente distorcidas. Inclui também a proposição e a verificação de um algoritmo capaz de calcular os índices (selecionados durante a revisão das normas) que pudessem avaliar a qualidade de energia através de sinais de tensão e corrente. Para o desenvolvimento do protótipo, foram selecionados sensores de tensão e de corrente confiáveis para o sistema de aquisição; um DSP, que executa os algoritmos previamente simulados, processando em tempo real os sinais adquiridos pelos sensores, a fim de reportar o estado da rede elétrica e/ou eventos ocorridos na rede através de um módulo ZigBee, responsável pela transmissão desses dados de forma segura. A classe de eventos de variação de tensão de curta duração foi incluída no processamento em tempo real realizado pelo DSP. Devido à imprevisibilidade e à rapidez da ocorrência desses eventos, foi desenvolvida uma ferramenta capaz de gerar essa classe de eventos, o gerador de VTCD. A análise de QEE em tempo real se mostrou viável mesmo com a utilização de dispositivos de baixo custo, permitindo, ainda que com algumas limitações, o levantamento de informações de QEE às quais cargas conhecidas estavam submetidas. / The present document includes a comprehensive literature review on power quality issues, to keep the development of this project aligned with national and international standards related; simulation algorithms such as FFT and CFA in order to verify the feasibility of its use, as well as limitations associated with the processing of strongly distorted waveform. It also includes the proposal and verification of an algorithm able to calculate the indices (selected during the standards review) that could assess the power quality through voltage and current signals. For prototype development, voltage and current sensors were selected for reliable acquisition system; a DSP, which running the previously simulated algorithms in order to process in real time the acquired voltage and current signals provided by sensors in order to report the status of the mains grid and/or events occurrence on the network through a ZigBee module, responsible for safety transmission data. The short term voltage change events class was also included in the real time processing performed by the DSP. Due to the unpredictability and short duration of these events, it was developed a tool capable of generating this class of events, the STVC generator. The PQ analysis in real time was feasible even with the use of low cost devices, allowing, although with some limitations, the survey of PQ information which known loads was submitted.