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In the development of wireless sensor networks, the lifetime of a sensor node is always a key design consideration. Since the battery in a sensor node can usually not be recharged or changed, power management is an effective way to extend the network lifetime. The wireless transceiver, also regarded as the ‘main radio’, is a relatively power hungry component in a sensor node. Therefore, an auxiliary always-on hardware ‘wakeup radio’ was proposed in order to reduce the overall power consumption. The wakeup radio listens to the wireless channel whereas the main radio is only active for a rather short time when the wakeup radio receives the packet with a certain pattern. Consequently, the power efficiency becomes a primary concern in the design of wakeup radio. This thesis focuses on the low power design and implementation of a digital b...
This dissertation proposes strategies not only for modelling price behavior in the dry bulk market, but also for modelling relationships between economic and technical variables of dry bulk ships, by using modern time series approaches, Monte Carlo simulation and other economic techniques. The time series modelling techniques, described extensively in Appendix A, primarily consist of the Vector Error Correction model (VECM), the Vector Autoregressive model (VAR), the Autoregressive and Moving Average model (ARMA), the General Autoregressive Conditional Heteroskedasticity model (GARCH), and their extensions. What are new and interesting here of the modelling strategies and their applications lie in several aspects: • Modelling variables of dry bulk ships: the relationships between the economic performance and technical specifica...
Investigations on the size of suspended particulate matter in the North Sea and two adjacent estuaries were carried out using an in situ technique: image analysis of photographs from an underwater camera system. The results obtained from such an in situ method gave a new knowledge on the size distributions of particles formed in natural waters, which is essential for understanding flocculation as well as for verifying models proposed to describe and simulate its processes.
For the class of equalizers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equalizer (DFE). The complexity of the Bayesian DFE however increases exponentially with the length of the channel impulse response (CIR) and the size of the symbol constellation. Conventional Monte Carlo simulation for evaluation the symbol error rate (SER) of the Bayesian DFE becomes impossible for high channel signal to noise ratio (SNR) conditions. It has been noted that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the SNR tends to infinity. This asymptotic property can be exploited for efficient simulation of the Bayesia...
A locally regularized orthogonal least squares (LROLS) algorithm is proposed for constructing parsimonious or sparse regression models that generalize well. By associating each orthogonal weight in the regression model with an individual regularization parameter, the ability for the orthogonal least squares (OLS) model selection to produce a very sparse model with good generalization performance is greatly enhanced. Furthermore, with the assistance of local regularization, when to terminate the model selection procedure becomes much clearer. This LROLS algorithm has computational advantages over the recently introduced relevance vector machine (RVM) method.
System identification using infinite-impulse-response (IIR) model is considered. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are preferred in order to avoid local minima. An efficient global optimisation method, called the adaptive simulated annealing (ASA), is adopted, and a new batch-recursive ASA algorithm is developed for on-line identification. Simulation study shows that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the ASA offers a viable tool to IIR model identification.
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