In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.

Aquesta Tesi Doctoral se centra en el disseny d'un nou linealitzador de Predistorsió Digital (Digital Predistortion - DPD) capaç de compensar la dinàmica i els efectes no lineals introduïts pels Amplificadors de Potència (Power Amplifiers - PAs). Un dels trets més rellevants d'aquest nou predistorsionador digital i adaptatiu consisteix en ser deduïble a partir d'un model de PA anomenat Nonlinear Auto-Regressive Moving Average (NARMA). A més, la seva arquitectura multi-LUT (multi-Taula) permet la implementació en un dispositiu Field Programmable Gate Array (FPGA).La funció de predistorsió es realitza en banda base, per tant, és independent de la banda freqüencial on es durà a terme l'amplificació del senyal de RF, el que pot resultar útil si tenim en compte escenaris multibanda o reconfigurables. D'altra banda, el fet que aquest DPD tingui en compte els efectes de memòria introduïts pel PA, representa una clara millora de les prestacions aconseguides per un simple DPD sense memòria. En comparació amb d'altres DPDs basats en models més computacionalment complexos, com és el cas de les xarxes neuronals amb memòria (Time-Delayed Neural Networks - TDNN), la estructura recursiva del DPD proposat permet reduir el nombre de LUTs necessàries per compensar els efectes de memòria del PA. A més, la seva estructura multi-LUT permet l'escalabilitat, és a dir, activar or desactivar les LUTs que formen el DPD en funció de la dinàmica que presenti el PA.En una primera aproximació al disseny del DPD, és necessari identificar el model NARMA del PA. Un dels majors avantatges que presenta el model NARMA és la seva capacitat per trobar un compromís entre la fidelitat en l'estimació del PA i la complexitat computacional introduïda. Per reforçar aquest compromís, l' ús d'algoritmes heurístics de cerca, com són el Simulated Annealing o els Genetic Algorithms, s'utilitzen per trobar els retards que millor caracteritzen la memòria del PA i per tant, permeten la reducció del nombre de coeficients necessaris per caracteritzar-la. Tot i així, la naturalesa recursiva del model NARMA comporta que, de cara a garantir l'estabilitat final del DPD, cal dur a terme un estudi previ sobre l'estabilitat del model.Una vegada s'ha obtingut el model NARMA del PA i s'ha verificat l'estabilitat d'aquest, es procedeix a l'obtenció de la funció de predistorsió a través del mètode d'identificació predictiu. Aquest mètode es basa en la continua identificació del model NARMA del PA i posteriorment, a partir del model obtingut, es força al PA perquè es comporti de manera lineal. Per poder implementar la funció de predistorsió en la FPGA, cal primer expressar-la en forma de combinacions en paral·lel i cascada de les anomenades Cel·les Bàsiques de Predistorsió (BPCs), que són les unitats fonamentals que composen el DPD. Una BPC està formada per un multiplicador complex, un port RAM dual que actua com a LUT (taula de registres) i un calculador d'adreces. Les LUTs s'omplen tenint en compte una distribució uniforme dels continguts i l'indexat d'aquestes es duu a terme mitjançant el mòdul de l'envoltant del senyal. Finalment, l'adaptació del DPD consisteix en monitoritzar els senyals d'entrada i sortida del PA i anar duent a terme actualitzacions periòdiques del contingut de les LUTs que formen les BPCs. El procés d'adaptació del contingut de les LUTs es pot dur a terme en la mateixa FPGA encarregada de fer la funció de predistorsió, o de manera alternativa, pot ser duta a terme per un dispositiu extern (com per exemple un DSP - Digital Signal Processor) en una escala de temps més relaxada. Per validar l'exposició teòrica i provar el bon funcionalment del DPD proposat en aquesta Tesi, es proporcionen resultats tant de simulació com experimentals que reflecteixen els objectius assolits en la linealització del PA. A més, certes qüestions derivades de la implementació pràctica, tals com el consum de potència o la eficiència del PA, són també tractades amb detall. / This Ph.D. thesis addresses the design of a new Digital Predistortion (DPD) linearizer capable to compensate the unwanted nonlinear and dynamic behavior of power amplifiers (PAs). The distinctive characteristic of this new adaptive DPD is its deduction from a Nonlinear Auto Regressive Moving Average (NARMA) PA behavioral model and its particular multi look-up table (LUT) architecture that allows its implementation in a Field Programmable Gate Array (FPGA) device.The DPD linearizer presented in this thesis operates at baseband, thus becoming independent on the final RF frequency band and making it suitable for multiband or reconfigurable scenarios. Moreover, the proposed DPD takes into account PA memory effects compensation which representsan step forward in overcoming classical limitations of memoryless predistorters. Compared to more computational complex DPDs with dynamic compensation, such Time-Delayed Neural Networks (TDNN), this new DPD takes advantage of the recursive nature of the NARMA structure to relax the number of LUTs required to compensate memory effects in PAs. Furthermore, its parallel multi-LUT architecture is scalable, that is, permits enabling or disabling the contribution of specific LUTs depending on the dynamics presented by a particular PA.In a first approach, it is necessary to identify a NARMA PA behavioral model. The extraction of PA behavioral models for DPD linearization purposes is carried out by means of input and output complex envelope signal observations. One of the major advantages of the NARMA structure regards its capacity to deal with the existing trade-off between computational complexity and accuracy in PA behavioral modeling. To reinforce this compromise, heuristic search algorithms such the Simulated Annealing or Genetic Algorithms are utilized to find the best sparse delays that permit accurately reproducing the PA nonlinear dynamic behavior. However, due to the recursive nature of the NARMA model, an stability test becomes a previous requisite before advancing towards DPD linearization.Once the PA model is identified and its stability verified, the DPD function is extracted applying a predictive predistortion method. This identification method relies just on the PA NARMA model and consists in adaptively forcing the PA to behave as a linear device. Focusing in the DPD implementation, it is possible to map the predistortion function in a FPGA, but to fulfill this objective it is first necessary to express the predistortion function as a combined set of LUTs.In order to store the DPD function into a FPGA, it has to be stated in terms of parallel and cascade Basic Predistortion Cells (BPCs), which are the fundamental building blocks of the NARMA based DPD. A BPC is formed by a complex multiplier, a dual port RAM memory block acting as LUT and an address calculator. The LUT contents are filled following an uniform spacing procedure and its indexing is performed with the amplitude (modulus) of the signal's envelope.Finally, the DPD adaptation consists in monitoring the input-output data and performing frequent updates of the LUT contents that conform the BPCs. This adaptation process can be carried out in the same FPGA in charge of performing the DPD function, or alternatively can be performed by an external device (i.e. a DSP device) in a different time-scale than real-time operation.To support all the theoretical design and to prove the linearization performance achieved by this new DPD, simulation and experimental results are provided. Moreover, some issues derived from practical experimentation, such as power consumption and efficiency, are also reported and discussed within this thesis.

In this thesis two different problems in distributed sensor networks are considered. Part I involves optimal quantiser design for decentralised estimation of a two-state hidden Markov model with dual sensors. The notion of optimality for quantiser design is based on minimising the probability of error in estimating the hidden Markov state. Equations for the filter error are derived for the continuous (unquantised) sensor outputs (signals), which are used to benchmark the performance of the quantisers. Minimising the probability of filter error to obtain the quantiser breakpoints is a difficult problem therefore an alternative method is employed. The quantiser breakpoints are obtained by maximising the mutual information between the quantised signals and the hidden Markov state. This method is known to work well for the single sensor case. Cases with independent and correlated noise across the signals are considered. The method is then applied to Markov processes with Gaussian signal noise, and further investigated through simulation studies. Simulations involving both independent and correlated noise across the sensors are performed and a number of interesting new theoretical results are obtained, particularly in the case of correlated noise. In Part II, the focus shifts to the detection of faults in helicopter transmission systems. The aim of the investigation is to determine whether the acoustic signature can be used for fault detection and diagnosis. To investigate this, statistical change detection algorithms are applied to acoustic vibration data obtained from the main rotor gearbox of a Bell 206 helicopter, which is run at high load under test conditions.

Uncertainty involved in the safe and comfort design of the structures is a major concern of civil engineers. Traditionally, the uncertainty has been overcome by utilizing various and relatively large safety factors for loads and structural properties. As a result in conventional design of for example tall buildings, the designed structural elements have unnecessary dimensions that sometimes are more than double of the ones needed to resist normal loads. On the other hand the requirements for strength and safety and comfort can be conflicting. Consequently, an alternative approach for design of the structures may be of great interest in design of safe and comfort structures that also offers economical advantages. Recently, there has been growing interest among the researchers in the concept of structural control as an alternative or complementary approach to the existing approaches of structural design. A few buildings have been designed and built based on this concept. The concept is to utilize a device for applying a force (known as control force) to encounter the effects of disturbing forces like earthquake force. However, the concept still has not found its rightful place among the practical engineers and more research is needed on the subject. One of the main problems in structural control is to find a proper algorithm for determining the optimum control force that should be applied to the structure. The investigation reported in this thesis is concerned with the application of active control to civil engineering structures. From the literature on control theory. (Particularly literature on the control of civil engineering structures) problems faced in application of control theory were identified and classified into two categories: 1) problems common to control of all dynamical systems, and 2) problems which are specially important in control of civil engineering structures. It was concluded that while many control algorithms are suitable for control of dynamical systems, considering the special problems in controlling civil structures and considering the unique future of structural control, many otherwise useful control algorithms face practical problems in application to civil structures. Consequently a set of criteria were set for judging the suitability of the control algorithms for use in control of civil engineering structures. Various types of existing control algorithms were investigated and finally it was concluded that predictive optimal control algorithms possess good characteristics for purpose of control of civil engineering structures. Among predictive control algorithms, those that use ARMA stochastic models for predicting the ground acceleration are better fitted to the structural control environment because all the past measured excitation is used to estimate the trends of the excitation for making qualified guesses about its coming values. However, existing ARMA based predictive algorithms are devised specially for earthquake and require on-line measurement of the external disturbing load which is not possible for dynamic loads like wind or blast. So, the algorithms are not suitable for tall buildings that experience both earthquake and wind loads during their life. Consequently, it was decided to establish a new closed loop predictive optimal control based on ARMA models as the first phase of the study. In this phase it was initially established that ARMA models are capable of predicting response of a linear SDOF system to the earthquake excitation a few steps ahead. The results of the predictions encouraged a search for finding a new closed loop optimal predictive control algorithm for linear SDOF structures based on prediction of the response by ARMA models. The second part of phase I, was devoted to developing and testing the proposed algorithm The new developed algorithm is different from other ARMA based optimal controls since it uses ARMA models for prediction of the structure response while existing algorithms predict the input excitation. Modeling the structure response as an AR or ARMA stochastic process is an effective mean for prediction of the structure response while avoiding measurement of the input excitation. ARMA models used in the algorithm enables it to avoid or reduce the time delay effect by predicting the structure response a few steps ahead. Being a closed loop control, the algorithm is suitable for all structural control conditions and can be used in a single control mechanism for vibration control of tall buildings against wind, earthquake or other random dynamic loads. Consequently the standby time is less than that for existing ARMA based algorithms devised only for earthquakes. This makes the control mechanism more reliable. The proposed algorithm utilizes and combines two different mathematical models. First model is an ARMA model representing the environment and the structure as a single system subjected to the unknown random excitation and the second model is a linear SDOF system which represents the structure subjected to a known past history of the applied control force only. The principle of superposition is then used to combine the results of these two models to predict the total response of the structure as a function of the control force. By using the predicted responses, the minimization of the performance index with respect to the control force is carried out for finding the optimal control force. As phase II, the proposed predictive control algorithm was extended to structures that are more complicated than linear SDOF structures. Initially, the algorithm was extended to linear MDOF structures. Although, the development of the algorithm for MDOF structures was relatively straightforward, during testing of the algorithm, it was found that prediction of the response by ARMA models can not be done as was done for SDOF case. In the SDOF case each of the two components of the state vector (i.e. displacement and velocity) was treated separately as an ARMA stochastic process. However, applying the same approach to each component of the state vector of a MDOF structure did not yield satisfactory results in prediction of the response. Considering the whole state vector as a multi-variable ARMA stochastic vector process yielded the desired results in predicting the response a few steps ahead. In the second part of this phase, the algorithm was extended to non-linear MDOF structures. Since the algorithm had been developed based on the principle of superposition, it was not possible to directly extend the algorithm to non-linear systems. Instead, some generalized response was defined. Then credibility of the ARMA models in predicting the generalized response was verified. Based on this credibility, the algorithm was extended for non-linear MDOF structures. Also in phase II, the stability of a controlled MDOF structure was proved. Both internal and external stability of the system were described and verified. In phase III, some problems of special interest, i.e. soil-structure interaction and control time delay, were investigated and compensated for in the framework of the developed predictive optimal control. In first part of phase III soil-structure interaction was studied. The half-space solution of the SSI effect leads to a frequency dependent representation of the structure-footing system, which is not fit for control purpose. Consequently an equivalent frequency independent system was proposed and defined as a system whose frequency response is equal to the original structure -footing system in the mean squares sense. This equivalent frequency independent system then was used in the control algorithm. In the second part of this phase, an analytical approach was used to tackle the time delay phenomenon in the context of the predictive algorithm described in previous chapters. A generalized performance index was defined considering time delay. Minimization of the generalized performance index resulted into a modified version of the algorithm in which time delay is compensated explicitly. Unlike the time delay compensation technique used in the previous phases of this investigation, which restricts time delay to be an integer multiplier of the sampling period, the modified algorithm allows time delay to be any non-negative number. However, the two approaches produce the same results if time delay is an integer multiplier of the sampling period. For evaluating the proposed algorithm and comparing it with other algorithms, several numerical simulations were carried during the research by using MATLAB and its toolboxes. A few interesting results of these simulations are enumerated below: ARM A models are able to predict the response of both linear and non-linear structures to random inputs such as earthquakes. The proposed predictive optimal control based on ARMA models has produced better results in the context of reducing velocity, displacement, total energy and operational cost compared to classic optimal control. Proposed active control algorithm is very effective in increasing safety and comfort. Its performance is not affected much by errors in the estimation of system parameters (e.g. damping). The effect of soil-structure interaction on the response to control force is considerable. Ignoring SSI will cause a significant change in the magnitude of the frequency response and a shift in the frequencies of the maximum response (resonant frequencies). Compensating the time delay effect by the modified version of the proposed algorithm will improve the performance of the control system in achieving the control goal and reduction of the structural response.

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis.