48 research outputs found

    System characterization and reception techniques for two-dimensional optical storage

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    Contributions to adaptive equalization and timing recovery for optical storage systems

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    Theory, design and application of gradient adaptive lattice filters

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    SIGLELD:D48933/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

    Adaptive equalisation for fading digital communication channels

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    This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts

    Linear and nonlinear adaptive filtering and their applications to speech intelligibility enhancement

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    Adaptive algorithms for nonstationary time series

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    Context-dependent fusion with application to landmine detection.

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    Traditional machine learning and pattern recognition systems use a feature descriptor to describe the sensor data and a particular classifier (also called expert or learner ) to determine the true class of a given pattern. However, for complex detection and classification problems, involving data with large intra-class variations and noisy inputs, no single source of information can provide a satisfactory solution. As a result, combination of multiple classifiers is playing an increasing role in solving these complex pattern recognition problems, and has proven to be viable alternative to using a single classifier. In this thesis we introduce a new Context-Dependent Fusion (CDF) approach, We use this method to fuse multiple algorithms which use different types of features and different classification methods on multiple sensor data. The proposed approach is motivated by the observation that there is no single algorithm that can consistently outperform all other algorithms. In fact, the relative performance of different algorithms can vary significantly depending on several factions such as extracted features, and characteristics of the target class. The CDF method is a local approach that adapts the fusion method to different regions of the feature space. The goal is to take advantages of the strengths of few algorithms in different regions of the feature space without being affected by the weaknesses of the other algorithms and also avoiding the loss of potentially valuable information provided by few weak classifiers by considering their output as well. The proposed fusion has three main interacting components. The first component, called Context Extraction, partitions the composite feature space into groups of similar signatures, or contexts. Then, the second component assigns an aggregation weight to each detector\u27s decision in each context based on its relative performance within the context. The third component combines the multiple decisions, using the learned weights, to make a final decision. For Context Extraction component, a novel algorithm that performs clustering and feature discrimination is used to cluster the composite feature space and identify the relevant features for each cluster. For the fusion component, six different methods were proposed and investigated. The proposed approached were applied to the problem of landmine detection. Detection and removal of landmines is a serious problem affecting civilians and soldiers worldwide. Several detection algorithms on landmine have been proposed. Extensive testing of these methods has shown that the relative performance of different detectors can vary significantly depending on the mine type, geographical site, soil and weather conditions, and burial depth, etc. Therefore, multi-algorithm, and multi-sensor fusion is a critical component in land mine detection. Results on large and diverse real data collections show that the proposed method can identify meaningful and coherent clusters and that different expert algorithms can be identified for the different contexts. Our experiments have also indicated that the context-dependent fusion outperforms all individual detectors and several global fusion methods

    Techniques of detection, estimation and coding for fading channels

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    The thesis describes techniques of detection, coding and estimation, for use in high speed serial modems operating over fading channels such as HF radio and land mobile radio links. The performance of the various systems that employ the above techniques are obtained via computer simulation tests. A review of the characteristics of HF radio channels is first presented, leading to the development of an appropriate channel model which imposes Rayleigh fading on the transmitted signal. Detection processes for a 4.8 kbit/s HF radio modem are then discussed, the emphasis, here, being on variants of the maximum likelihood detector that is implemented by the Viterbi algorithm. The performance of these detectors are compared with that of a nonlinear equalizer operating under the same conditions, and the detector which offers the best compromise between performance and complexity is chosen for further tests. Forward error correction, in the form of trellis coded modulation, is next introduced. An appropriate 8-PSK coded modulation scheme is discussed, and its operation over the above mentioned HF radio modem is evaluated. Performance comparisons are made of the coded and uncoded systems. Channel estimation techniques for fast fading channels akin to cellular land mobile radio links, are next discussed. A suitable model for a fast fading channel is developed, and some novel estimators are tested over this channel. Computer simulation tests are also used to study the feasibility of the simultaneous transmission of two 4-level QAM signals occupying the same frequency band, when each of these signals are transmitted at 24 kbit/s over two independently fading channels, to a single receiver. A novel combined detector/estimator is developed for this purpose. Finally, the performance of the complete 4.8 kbit/s HF radio modem is obtained, when all the functions of detection, estimation and prefiltering are present, where the prefilter and associated processor use a recently developed technique for the adjustment of its tap gains and for the estimation of the minimum phase sampled impulse response

    Vibration Monitoring: Gearbox identification and faults detection

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    L'abstract è presente nell'allegato / the abstract is in the attachmen
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