A kurtosis-driven variable step-size LMS algorithm

Published versio

Discrete-time variance tracking with application to speech processing

Two new discrete-time algorithms are presented for tracking variance and reciprocal variance. The closed loop nature of the solutions to these problems makes this approach highly accurate and can be used recursively in real time. Since the Least-Mean Squares (LMS) method of parameter estimation requires an estimate of variance to compute the step size, this technique is well suited to applications such as speech processing and adaptive filtering

Blind equalization

An equalizer is an adaptive filter that compensates for the non-ideal characteristics of a communication channel by processing the received signal. The adaptive algorithm searches for the inverse impulse response of the channel, and it requires knowledge of a training sequence, in order to generate an error signal necessary for the adaptive process. There are practical situations where it would be highly desirable to achieve complete adaptation without the use of a training sequence, hence the the term blind . Examples of these situations are multipoint data networks, high-capacity line-of-sight digital radio, and reflection seismology. A blind adaptive algorithm has been developed, based on simplified equalization criteria. These criteria are that the second- and fourth-order moments of the input and output sequences are equalized. The algorithm is entirely driven by statistics, only requiring knowledge of the variance of the input signal. Because of the insensitivity of higher-order statistics to Gaussian processes, the algorithm performs well when additive white Gaussian noise is present in the channel. Simulations are presented in which the new blind equalizer developed is compared to other equalization algorithms

A comparative study of the effectiveness of vibration and acoustic emission in diagnosing a defective bearing in a planetry gearbox

Whilst vibration analysis of planetary gearbox faults is relatively well established, the application of Acoustic Emission (AE) to this field is still in its infancy. For planetary-type gearboxes it is more challenging to diagnose bearing faults due to the dynamically changing transmission paths which contribute to masking the vibration signature of interest. The present study is aimed to reduce the effect of background noise whilst extracting the fault feature from AE and vibration signatures. This has been achieved through developing of internal AE sensor for helicopter transmission system. In addition, series of signal processing procedure has been developed to improved detection of incipient damage. Three signal processing techniques including an adaptive filter, spectral kurtosis and envelope analysis, were applied to AE and vibration data acquired from a simplified planetary gearbox test rig with a seeded bearing defect. The results show that AE identified the defect earlier than vibration analysis irrespective of the tortuous transmission pat

Signal Processing Design of Low Probability of Intercept Waveforms

This thesis investigates a modification to Differential Phase Shift Keyed (DPSK) modulation to create a Low Probability of Interception/Exploitation (LPI/LPE) communications signal. A pseudorandom timing offset is applied to each symbol in the communications stream to intentionally create intersymbol interference (ISI) that hinders accurate symbol estimation and bit sequence recovery by a non-cooperative receiver. Two cooperative receiver strategies are proposed to mitigate the ISI due to symbol timing offset: a modified minimum Mean Square Error (MMSE) equalization algorithm and a multiplexed bank of equalizer filters determined by an adaptive Least Mean Square (LMS) algorithm. Both cooperative receivers require some knowledge of the pseudorandom symbol timing dither to successfully demodulate the communications waveform. Numerical Matlab® simulation is used to demonstrate the bit error rate performance of cooperative receivers and notional non-cooperative receivers for binary, 4-ary, and 8-ary DPSK waveforms transmitted through a line-of-sight, additive white Gaussian noise channel. Simulation results suggest that proper selection of pulse shape and probability distribution of symbol timing offsets produces a waveform that is accurately demodulated by the proposed cooperative receivers and significantly degrades non-cooperative receiver symbol estimation accuracy. In typical simulations, non-cooperative receivers required 2-8 dB more signal power than cooperative receivers to achieve a bit error rate of 1.0%. For nearly all reasonable parameter selections, non-cooperative receivers produced bit error rates in excess of 0.1%, even when signal power is unconstrained

Blind adaptive constrained reduced-rank parameter estimation based on constant modulus design for CDMA interference suppression

This paper proposes a multistage decomposition for blind adaptive parameter estimation in the Krylov subspace with the code-constrained constant modulus (CCM) design criterion. Based on constrained optimization of the constant modulus cost function and utilizing the Lanczos algorithm and Arnoldi-like iterations, a multistage decomposition is developed for blind parameter estimation. A family of computationally efficient blind adaptive reduced-rank stochastic gradient (SG) and recursive least squares (RLS) type algorithms along with an automatic rank selection procedure are also devised and evaluated against existing methods. An analysis of the convergence properties of the method is carried out and convergence conditions for the reduced-rank adaptive algorithms are established. Simulation results consider the application of the proposed techniques to the suppression of multiaccess and intersymbol interference in DS-CDMA systems

Remote online machine condition monitoring using advanced internet, wireless and mobile communication technologies

A conceptual model with wireless and mobile techniques is developed in this thesis for remote real-time condition monitoring, which is applied for monitoring, diagnosing, and controlling the working conditions of machines. The model has the following major functions: data acquisition, data processing, decision making, and remote communication. The data acquisition module is built up within this model using the sensory technique and data I/O interfaces to acquire the working conditions data of a machine and extract the physical information about the machine (e.g. failure, wear, etc.) for data processing and decision making. The data processing is conducted using digital conversion and feature extraction to process the received analogue condition data and convert the data into the physical quantities of working condition of the machine for sequent fault diagnosis. A real-time fault diagnostic scheme for decision-making is applied based on digital filtering and pattern classification to real-time identify the fault symptom of the machine and provide advice for decision making for maintenance. Process control is implemented to control the operation status of the machine automatically, inform the maintenance personnel diagnostic results and alert the working conditions of the machine. Remote communication with wireless and mobile features greatly advance the machine’s condition monitoring technology with real-time fault diagnostic capacity, by providing a wireless-based platform to enable the implementation of data acquisition, real-time fault diagnosis, and decision making through the Internet, wireless, and mobile phone network. The model integrating above techniques and methods has been applied into the following three areas: (1) Development of a Remote Real-time Condition Monitoring System of Industrial Gearbox, supported by the Stimulation Innovation Success programme (2007-2008); (2) Development of a Remote Control System of Solid Desiccant Dehumidifier for Air Conditioning in Low Carbon Emission Buildings, supported by the Sustainable Construction iNET programme (2009-2010); (3) Development of an Innovative Remote Monitoring System of Thermo-Electric-Generations, supported by the Sustainable Construction iNET programme (2010-2011). The combination of wireless and mobile techniques with data acquisition, real-time fault diagnosis, and decision-making, into a model for remote real-time condition monitoring is a novel contribution to this area

Modelling and engineering artificial burnt-bridge ratchet molecular motors

Nature has evolved many mechanisms for achieving directed motion on the subcellular level. The burnt-bridge ratchet (BBR) is one mechanism used to accomplish superdiffusive motion over long distances via the successive cleavage of surface-bound energy-rich substrate sites. The BBR mechanism is utilized throughout Nature: it can be found in bacteria, plants, mammals, arthropods (for example Crustaceans and Cheliceratans), as well as non-life forms such as influenza. Motivated to understand how fundamental engineering principles alter BBR kinetics, we have built both computer models and synthetic experimental systems to understand BBR kinetics. By exploring the dynamics of BBRs through simulation we find that their motor-like properties are highly dependent on the number of catalytic legs, the distance that the legs can reach from the central hub, and the hub topology. We further explore how design features in the underlying landscape affect BBR dynamics. We find that reducing the landscape from two- to one-dimensional increases superdiffusivity but leads to a loss in processivity. We also find that landscape elasticity affects all motor-like dynamical properties of BBRs: there are different optimal stiffnesses for distinct dynamical characteristics. For a spherical-hub BBR, speed, processivity, and persistence length are optimized at high, intermediate and soft stiffnesses, respectively, while rolling is also optimized at a high surface stiffness. Towards our development of a novel micron-sized protein-based BBR in the lab, we develop a new surface chemistry passivation technique and apply it to the surface of nanowires, turning an array of waveguiding nanowires into a high-throughput biosensing assay. In a separate assay, our protein-based BBR, which we call the lawnmower, is implemented in two dimensions on glass cover slips prepared with our surface chemistry (which serves as the lawn). We find the lawnmower dynamics reproduce key observations found in other similar systems, such as saltatory motion and broadly varying anomalously diffusive behaviour. The successful implementation of the lawnmower marks the first demonstration of an artificial protein-based molecular motor