Symbol Synchronization for SDR Using a Polyphase Filterbank Based on an FPGA

This paper is devoted to the proposal of a highly efficient symbol synchronization subsystem for Software Defined Radio. The proposed feedback phase-locked loop timing synchronizer is suitable for parallel implementation on an FPGA. The polyphase FIR filter simultaneously performs matched-filtering and arbitrary interpolation between acquired samples. Determination of the proper sampling instant is achieved by selecting a suitable polyphase filterbank using a derived index. This index is determined based on the output either the Zero-Crossing or Gardner Timing Error Detector. The paper will extensively focus on simulation of the proposed synchronization system. On the basis of this simulation, a complete, fully pipelined VHDL description model is created. This model is composed of a fully parallel polyphase filterbank based on distributed arithmetic, timing error detector and interpolation control block. Finally, RTL synthesis on an Altera Cyclone IV FPGA is presented and resource utilization in comparison with a conventional model is analyzed

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Global Nonlinear Kernel Prediction for Large Dataset with a Particle Swarm Optimized Interval Support Vector Regression

A new global nonlinear predictor with a particle swarm-optimized interval support vector regression (PSO-ISVR) is proposed to address three issues (viz., kernel selection, model optimization, kernel method speed) encountered when applying SVR in the presence of large data sets. The novel prediction model can reduce the SVR computing overhead by dividing input space and adaptively selecting the optimized kernel functions to obtain optimal SVR parameter by PSO. To quantify the quality of the predictor, its generalization performance and execution speed are investigated based on statistical learning theory. In addition, experiments using synthetic data as well as the stock volume weighted average price are reported to demonstrate the effectiveness of the developed models. The experimental results show that the proposed PSO-ISVR predictor can improve the computational efficiency and the overall prediction accuracy compared with the results produced by the SVR and other regression methods. The proposed PSO-ISVR provides an important tool for nonlinear regression analysis of big data

Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models

Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called \textit{ensemble of near isotonic regression} (ENIR). The method can be considered as an extension of BBQ, a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression. ENIR is designed to address the key limitation of isotonic regression which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be combined with many existing classification models. We demonstrate the performance of ENIR on synthetic and real datasets for the commonly used binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is $O(N \log N)$ time, where $N$ is the number of samples

Design Simulation and Experiments on Electrical Machines for Integrated Starter-Generator Applications

This thesis presents two different non-permanent magnet machine designs for belt-driven integrated starter-generator (B-ISG) applications. The goal of this project is to improve the machine performance over a benchmark classical switched reluctance machine (SRM) in terms of efficiency, control complexity, torque ripple level and power factor. The cost penalty due to the necessity of a specially designed H-bridge machine inverter is also taken into consideration by implementation of a conventional AC inverter. The first design changes the classical SRM winding configuration to utilise both self-inductance and mutual-inductance in torque production. This allows the use of AC sinusoidal current with lower cost and comparable or even increased torque density. Torque density can be further increased by using a bipolar square current drive with optimum conduction angle. A reduction in control difficulty is also achieved by adoption of standard AC machine control theory. Despite these merits, the inherently low power factor and poor field weakening capability makes these machines unfavourable in B-ISG applications. The second design is a wound rotor synchronous machine (WRSM). From FE analysis, a six pole geometry presents a lower loss level over four pole geometry. Torque ripple and iron loss are effectively reduced by the use of an eccentric rotor pole. To determine the minimum copper loss criteria, a novel algorithm is proposed over the conventional Lagrange method, where the deviation is lowered from ± 10% to ± 1%, and the simulation time is reduced from hours to minutes on standard desktop PC hardware. With the proposed design and control strategies, the WRSM delivers a comparable field weakening capability and a higher efficiency compared with the benchmark SRM under the New European Driving Cycle, where a reduction in machine losses of 40% is possible. Nevertheless, the wound rotor structure brings mechanical and thermal challenges. A speed limit of 11,000 rpm is imposed by centrifugal forces. A maximum continuous motoring power of 3.8 kW is imposed by rotor coil temperature performance, which is extended to 5 kW by a proposed temperature balancing method. A prototype machine is then constructed, where the minimum copper loss criteria is experimentally validated. A discrepancy of no more than 10% is shown in back-EMF, phase voltage, average torque and loss from FE simulation