An efficient low power FFT implementation for multiband full-rate ultra-wideband (UWB) receivers

This paper discusses the design, implementation and synthesis of an FFT module that has been specifically optimized for use in the OFDM based Multiband UWB system, although the work is generally applicable to many other OFDM based receiver systems. Previous work has detailed the requirements for the receiver FFT module within the Multiband UWB ODFM based system and this paper draws on those requirements coupled with modern digital architecture principles and low power design criteria to converge on our optimized solution. The FFT design obtained in this paper is also applicable for implementation of the transmitter IFFT module therefore only needing one FFT module for half-duplex operation. The results from this paper enable the baseband designers of the 200Mbit/sec variant of Multiband UWB systems (and indeed other OFDM based receivers) using System-on-Chip (SoC), FPGA and ASIC technology to create cost effective and low power solutions biased toward the competitive consumer electronics market.

Soft-bit and numerical precision requirements on multiband full-rate ultra-wideband (UWB) receivers

This paper discusses the architectural design, implementation and associated simulated peformance results of a possible receiver solution fir a multiband Ultra-Wideband (UWB) receiver. The paper concentrates on the tradeoff between the soft-bit width and numerical precision requirements for the receiver versus performance. The required numerical precision results obtained in this paper can be used by baseband designers of cost effective UWB systems using Systein-on-Chip (SoC), FPGA and ASIC technology solutions biased toward the competitive consumer electronics market(1).

Increasing detection rate of user-to-root attacks using genetic algorithms

An extensive set of machine learning and pattern classification techniques trained and tested on KDD dataset failed in detecting most of the user-to-root attacks. This paper aims to provide an approach for mitigating negative aspects of the mentioned dataset, which led to low detection rates. Genetic algorithm is employed to implement rules for detecting various types of attacks. Rules are formed of the features of the dataset identified as the most important ones for each attack type. In this way we introduce high level of generality and thus achieve high detection rates, but also gain high reduction of the system training time. Thenceforth we re-check the decision of the user-to- root rules with the rules that detect other types of attacks. In this way we decrease the false-positive rate. The model was verified on KDD 99, demonstrating higher detection rates than those reported by the state- of-the-art while maintaining low false-positive rate.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.