nÃo hà / Swarming is the mass exodus of bees in a hive, whose most common causes are lack of food, stress, variations of humidity and especially high temperatures. Among the types of swarming, one in which the complete abandonment of the hive occurs has brought great harm to Brazilian beekeepers, particularly the Northeast. In the Northeast region, of great importance for the Brazilian beekeeping, and where high temperatures are common in most of the year, a large number of hives is lost due to the swarming through abandonment. In an attempt to mitigate this problem, we propose a proactive monitoring hives via a network of wireless sensors capable of identifying atypical heating indicative of a preswarming condition. By means of a sampling pattern obtained from the cyclical daily temperatures, we developed a predictive algorithm based on pattern recognition techniques capable of detecting the increase of temperature inside the beehive (microclimate) responsible for the typical stress bees culminating in swarming. Such a mechanism is also able to recognize and avoid sending redundant information over the network in order to reduce radio communication, thereby reducing costs of data transmission and energy. / EnxameaÃÃo à a saÃda em massa das abelhas de uma colmeia, cujas causas mais comuns sÃo a falta de alimentos, estresse, variaÃÃes da umidade do ar e principalmente as altas temperaturas. Dentre os tipos de enxameaÃÃo, aquela em que ocorre o abandono completo da colmeia tem trazido grandes prejuÃzos aos apicultores brasileiros, particularmente aos nordestinos. Na regiÃo Nordeste, de grande importÃncia para a produÃÃo apÃcola brasileira e onde altas temperaturas sÃo comuns na maior parte do ano, um grande nÃmero de colmeias à perdido em funÃÃo da enxameaÃÃo por abandono. Na tentativa de mitigar este problema, propomos aqui um monitoramento proativo de colmeias via uma rede de sensores sem fio capaz de identificar o aquecimento atÃpico indicativo de uma condiÃÃo prÃ-enxameatÃria. Por meio de um padrÃo de coletas obtido a partir do comportamento cÃclico de temperaturas diÃrias, elaboramos um algoritmo preditivo, baseado em tÃcnicas de reconhecimento de padrÃes, capaz de detectar o aumento da temperatura no interior da colmeia (microclima) responsÃvel pelo estresse tÃpico das abelhas que culmina na enxameaÃÃo. Tal mecanismo tambÃm à capaz de reconhecer e evitar o envio de informaÃÃes redundantes pela rede de modo a diminuir a comunicaÃÃo via rÃdio, consequentemente reduzindo custos de transmissÃo de dados e energia.

Though there has been a considerable amount of work investigating the early stages of low-mass star formation in recent years, the general theory is only broadly understood and several open questions remain. Specifically, the dominant physical mechanisms which connect large-scale molecular cloud structures, intermediate-scale filamentary gas flows, and small-scale collapsing prestellar envelopes in the interstellar medium are poorly constrained. Even for an individual forming protostar, the evolution of the mass accretion rate from the envelope onto the central object is debated with little observational evidence to help guide the theoretical framework. In addition, with the development of new technology such as the continuum imaging instrument in operation at the James Clerk Maxwell Telescope (JCMT), the Submillimetre Common User Bolometer Array 2 (SCUBA-2), the best practices for data reduction and image calibration for ground-based, submillimetre wavelength observations are still being investigated. In this dissertation, I address facets of these open questions in five main projects with an overarching focus on the flow of material from the largest to the smallest scales in a molecular cloud. By performing synthetic observations of a numerical simulation of a turbulent molecular cloud, I investigate the nature of prestellar envelopes and find evidence of larger mass reservoirs that form filamentary structures and feed cluster formation. Then, after robustly investigating and suggesting improvements for ground-based, submillimetre data reduction techniques, I continue to probe the connection between larger and smaller scales by characterising structure fragmentation in the Southern Orion A Molecular Cloud from the perspective of 850 m continuum data. Finally, I follow star forming material to even smaller scales by exploring the evolution of the mass accretion rate onto individual protostars. This examination has required designing and implementing unprecedented spatial alignment and flux calibration techniques at 850 m. Using these newly calibrated images, I am able to identify several candidate sources that show evidence for submillimetre variability, suggesting changes in protostellar accretion rates over several year timescales. / Graduate

M.Sc. / Light Detection and Ranging (LiDAR) provide decidedly accurate datasets with high data densities, in a very short time-span. However, the high volumes of data associated with LiDAR often require some form of data reduction to increase the data handling efficiency of these datasets, of which the latter could affect the feasibility of Digital Elevation Models (DEMs). Critically, when DEM processing times are reduced, the resultant DEM should still represent the terrain adequately. This study investigated three different data reduction techniques, (1) random point reduction, (2) grid resolution reduction, and (3) combined data reduction, in order to assess their effects on the accuracy, as well as the data handling efficiency of derived DEMs. A series of point densities of 1 %, 10 %, 25 %, 50 % and 75 % were interpolated along a range of horizontal grid resolutions (1-, 2-, 3-, 4-, 5-, 10- and 30- m). Results show that, irrespective of terrain complexity, data points can be randomly reduced up to 25 % of the data points in the original dataset, with minimal effects on the remaining dataset. However, when these datasets are interpolated, data points can only be reduced to 50 % of the original data points, before showing large deviations from the original DEM. A reduction of the grid resolution of DEMs showed that the grid resolution could be lowered to 4 metres before showing significant deviations. When combining point density reduction with grid resolution reduction, results indicate that DEMs can be derived from 75 % of the data points, at a grid resolution of 3 metres, without sacrificing more than 15 percent of the accuracy of the original DEM. Ultimately, data reduction should result in accurate DEMs that reduce the processing time. When considering the effect on the accuracy, as well as the processing times of the data reduction techniques, results indicate that resolution reduction is the most effective data reduction technique. When reducing the grid resolution to 4 metres, data handling efficiencies improved by 94 %, while only sacrificing 10 % of the data accuracy. Furthermore, this study investigated data reduction on a variety of terrain complexities and found that the reduction thresholds established by this study were applicable to both complex and non-complex terrain.

In planning for future electricity supplies certain issues will need to be considered such as increased energy usage, urbanisation, reduction in personnel, global warming and the conservation of natural resources. As the result, some countries have investigated the transformation of their existing power grid to the so-called Smart Grid. The Smart Grid has three main characteristics which are, to some degree, antagonistic. These characteristics are the provision of good power quality, energy cost reduction and improvement in the reliability of the grid. The need to ensure that they can be accomplished together demands much richer Information and Communications Technology (ICT) networks than the current systems available. In this research we have identified the gap in the current proposals for the ICT of the power grid. We have designed and developed an ICT architecture for the neighbourhood sub-Grid level of the electrical network, where monitoring at this level is very underdeveloped because most current grids are controlled centrally and the response of the neighbourhood area is not generally monitored or actively controlled. Our designed ICT architecture, which is based on established architectural principles, can incorporate data from heterogeneous sources. This layered architecture provides both the sensors that can directly measure the electrical activity of the network (e.g. voltage) and also the sensors that measure the environment (e.g. temperature) since these provide information that can be used to anticipate demand and improve control actions. Additionally, we have de-signed a visualisation tool as an interface for a grid operators to facilitate a better comprehension of the behaviour of the neighbourhood level of the Smart Grid. Since we have noticed that energy aware ICT is a prerequisite for an efficient Smart Grid, we have utilised two different approaches to tackle this issue. The first approach was to utilise a cluster-based communication technique for the second layer of the architecture, which comprises Wireless Sensor Networks, where energy limitation is the major problem. Accordingly, we have analysed the energy-aware topology for wireless sensor networks embedded in the mentioned layer. We provide evidence that the proposed topology will bring energy efficiency to the communication network of the Smart Grid. The second approach was to develop a data reduction algorithm to reduce the volume of data prior to data transmission. We demonstrated that our developed data reduction is suitable for Smart Grid applications which can keep the integrity and quality of data. Finally, the work presented in this thesis is based on a real project that is being implemented in the medium voltage power network of the University of Manchester where power grid instrumentation, real data and professionals in the field are available. Since the project is long-term and the environmental sensor networks in particular are not currently installed we have evaluated some of our predictions via simulation. However, where the instrumentation was available, we were able to compare our predictions and our simulations with actual experimental results.

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This thesis presents a shared memory parallel version of the hybrid classification algorithm IGSCR (iterative guided spectral class rejection), a novel data reduction technique that can be used in conjunction with PIGSCR (parallel IGSCR), a noise removal method based on the maximum noise fraction (MNF), and a continuous version of IGSCR (CIGSCR) that outputs soft classifications. All of the above are either classification algorithms or preprocessing algorithms necessary prior to the classification of high dimensional, noisy images. PIGSCR was developed to produce fast and portable code using Fortran 95, OpenMP, and the Hierarchical Data Format version 5 (HDF5) and accompanying data access library. The feature reduction method introduced in this thesis is based on the singular value decomposition (SVD). This feature reduction technique demonstrated that SVD-based feature reduction can lead to more accurate IGSCR classifications than PCA-based feature reduction. This thesis describes a new algorithm used to adaptively filter a remote sensing dataset based on signal-to-noise ratios (SNRs) once the maximum noise fraction (MNF) has been applied. The adaptive filtering scheme improves image quality as shown by estimated SNRs and classification accuracy improvements greater than 10%. The continuous iterative guided spectral class rejection (CIGSCR) classification method is based on the iterative guided spectral class rejection (IGSCR) classification method for remotely sensed data. Both CIGSCR and IGSCR use semisupervised clustering to locate clusters that are associated with classes in a classification scheme. This type of semisupervised classification method is particularly useful in remote sensing where datasets are large, training data are difficult to acquire, and clustering makes the identification of subclasses adequate for training purposes less difficult. Experimental results indicate that the soft classification output by CIGSCR is reasonably accurate (when compared to IGSCR), and the fundamental algorithmic changes in CIGSCR (from IGSCR) result in CIGSCR being less sensitive to input parameters that influence iterations. / Ph. D.

This dissertation focuses on problems in design, optimization and control of complex, large-scale dynamical systems from different viewpoints. The goal is to develop new algorithms and methods, that solve real problems more efficiently, together with providing mathematical insight into the success of those methods. There are three main contributions in this dissertation. In Chapter 3, we provide a new method to solve large-scale algebraic Riccati equations, which arise in optimal control, filtering and model reduction. We present a projection based algorithm utilizing proper orthogonal decomposition, which is demonstrated to produce highly accurate solutions at low rank. The method is parallelizable, easy to implement for practitioners, and is a first step towards a matrix free approach to solve AREs. Numerical examples for n >= 100,000 unknowns are presented. In Chapter 4, we develop a system identification method which is motivated by tangential interpolation. This addresses the challenge of fitting linear time invariant systems to input-output responses of complex dynamics, where the number of inputs and outputs is relatively large. The method reduces the computational burden imposed by a full singular value decomposition, by carefully choosing directions on which to project the impulse response prior to assembly of the Hankel matrix. The identification and model reduction step follows from the eigensystem realization algorithm. We present three numerical examples, a mass spring damper system, a heat transfer problem, and a fluid dynamics system. We obtain error bounds and stability results for this method. Chapter 5 deals with control and observation design for parameter dependent dynamical systems. We address this by using local parametric reduced order models, which can be used online. Data available from simulations of the system at various configurations (parameters, boundary conditions) is used to extract a sparse basis to represent the dynamics (via dynamic mode decomposition). Subsequently, a new compressed sensing based classification algorithm is developed which incorporates the extracted dynamic information into the sensing basis. We show that this augmented classification basis makes the method more robust to noise, and results in superior identification of the correct parameter. Numerical examples consist of a Navier-Stokes, as well as a Boussinesq flow application. / Ph. D.

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / Solid State On-Board Recording is becoming a revolutionary way of recording airborne telemetry data and IRIG 106 Chapter 10 “Solid State On-Board Recorder Standard” provides interface documentation for solid state digital data acquisition. The Reduction and Analysis Program for Telemetry Recordings (RAPTR) is a standardized and extensible software application developed by the 96th Communications Group, Test and Analysis Division, at Eglin AFB, and provides a data reduction capability for disk files in Chapter 10 format. This paper provides the system description and software architecture of RAPTR and presents the 96th Communication Group’s total solution for Chapter 10 telemetry data reduction.