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The use of 3D models in many multimedia applications is becoming widespread. In many of these applications, 3D models are often transmitted over communication networks which degrade the quality of the models. This thesis investigates error control strategies for the transmission of progressively compressed 3D models over error-prone wireless channels. In the first part of the thesis, the use of product channel codes for protecting the compressed 3D model bitstream is considered. In the scheme proposed therein, bit allocation between source and parity bits is done in a joint source-channel coding framework. Next, the use of power control for the protection of 3D models against channel errors is investigated. Taking the importance of the various levels of the compressed bistream into account, an unequal power allocation scheme is proposed. The proposed scheme allocates unequal amounts of transmit power levels to the various levels of the compressed bitstream in such a manner that the expected distortion in the decoded 3D model is minimized. Finally, the problem of transmitting 3D models over wireless networks with feedback channels is addressed. For this scenario, an error control system which combines a hybrid automatic repeat request scheme with a forward error correction scheme is proposed. With the proposed system, the base mesh is always delivered, thus guaranteeing a minimum quality of service for 3D model transmission. The effectiveness of the proposed schemes is demonstrated through simulations, which are assessed with the use of objective and subjective performance measures.

This thesis deals with joint channel and frequency offset estimation in many scenarios of wireless communications. In additive white Gaussian noise (AWGN) channels, a general literature survey of channel and frequency offset estimators based on the data-aided maximum likelihood (ML) principle is presented. The Cramer-Rao lower bounds (CRLB)s of the joint estimators are presented. Performance analysis of advanced frequency estimators recently proposed in the literature is provided. The performance of the estimators is compared for different application scenarios so that to get a better understanding of the differences, in terms of accuracy, complexity, frequency estimation range, signal to noise ratio (SNR) threshold. The dichotomous search (DS) frequency estimator is found to be the best practical choice. The DS frequency estimator emploies a fast Fourier transform (FFT)-based coarse search and dichotomous fine search of the periodogram peak to approximate the ML optimal estimator. This algorithm achieves the ML-like accuracy over a wide range of SNRs and throughout the wide frequency estimation range. As it relies entirely on linear operations, it is perfectly suitable for real-time implementation. In time-invariant frequency-selective channels, the joint data-aided estimation of channel and frequency offset for signals exploiting multipath diversity is considered. This diversity improves the estimation performance by searching for the peak of the combined periodograms of multipath components. The first estimator is based on the Bayesian approach and can be used when certain prior statistical knowledge about the channel is available. The second estimator is based on the ML approach and can operate when these channel statistics are not available. Both estimators employ the DS frequency estimation technique. These estimators have a high-accuracy performance with an estimation error very close to the CRLBs over a wide range of SNRs and throughout the wide frequency acquisition range. In frequency-flat time-variant fading channels, new joint data-aided channel and frequency offset estimators are derived. The proposed estimators are based on the basis expansion model (BEM) of the fading process and the DS frequency estimation technique. The first estimator is based on the Bayesian approach and exploits prior channel statistics to provide a high performance. The second estimator relies on the ML approach, and with a slightly lower accuracy, can operate when the prior statistics are unknown. The performance of the proposed joint estimators is examined for different scenarios in Rayleigh fading channels. The sensitivity of the Bayesian estimator to the knowledge of the Doppler frequency is investigated using such BEMs as Karhunen-Loeve (KL), discrete prolate spheroidal (DPS), generalised complex exponential (GCE), and B-spline (BS) functions. The BS-BEM is found to be the most robust and the best practical choice. In doubly-selective fading channels, a joint data-aided channel and frequency offset Bayesian estimator is proposed. The joint estimator is based on the BS-BEM representation of the fading process and the DS frequency estimation technique. Simulation results for different scenarios in Rayleigh fading channels show that the proposed estimator achieves a high accuracy performance, which is close to that with perfect knowledge of the frequency offset, over a wide range of SNRs, for different Doppler frequencies and throughout all the frequency acquisition range. Iterative turbo receivers are developed for frequency-flat time-variant fading channels which jointly perform channel and frequency offset estimation together with data detection and decoding. The estimation and detection are based on the BS-BEM of the fading time variations and use the DS frequency estimation. Soft information generated in the turbo decoder is used to improve the quality of detection in the subsequent iterations. Depending on how much knowledge of channel statistics is available, three versions of the joint estimator, the Bayesian, ML and regularised-ML (E-ML) are provided. Simulation results show that the proposed receivers provide as good performance as the corresponding ones operating with perfect knowledge of the frequency offset, and close to that operating with perfect channel knowledge.

The fundamental detection problem in fading channels involves the correct estimation of transmitted symbols at the receiver in the presence of Additive White Gaussian Noise (AWGN). Detection can be considered when the receiver is assumed not to know the channel (non-coherent detection), or alternatively, when the random channel is tracked at the receiver (coherent detection). It can be shown that for a given error probability, coherent detection schemes require a Signal to Noise Ratio (SNR) that is 3dB less than the SNR required for non-coherent detection schemes. It is also known that the performance of coherent detection schemes can be further improved using space-frequency diversity techniques, for example, when multiple-input multiple-output (MIMO) antenna technologies are employed in conjunction with Orthogonal Frequency Division Multiplexing (OFDM). However, the superior performance promised by the MIMO-OFDM technology relies on the availability of accurate Channel State Information (CSI) at the receiver. In the literature, the Mean Square Error (MSE) performance of MIMO-OFDM CSI estimators is known to be limited by the SNR. This thesis adopts a di®erent view to estimator performance, by evaluating the accuracy of CSI estimates as limited by the maximum delay spread of the multipath channel. These considerations are particularly warranted for high data rate multi-user MIMO-OFDM systems which deploy large numbers of transmit antennas at either end of the wireless link. In fact, overloaded multi-user CSI estimation can be e®ectively studied by considering the grouping together of the user antennas for the uplink while conversely, considering a small number of antennas due to size constraints for the downlink. Therefore, most of the work developed in this thesis is concerned with improving existing single-user MIMO-OFDM CSI estimators but the results can be extended to multi-user system.

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Modelling detailed electromagnetic interactions in Electromagnetic Compatibility predictions is an extremely demanding task, made more difficult by the increasing complexity of modem engineering problems. Over the last decade major innovations in numerical models and methods have been introduced to reduce demands on computational resources or render the simulations of large systems containing a diverse range of physical features possible. This thesis presents one of the methods of dealing with large systems which utilises the concept of sub-cells containing fine geometrical objects. A general approach to embedding fine features into a coarse numerical time-domain techniques such as the Transmission Line Modelling (TLM) method is proposed. A non-standard node has been developed that mimics the electro- magnetic behaviour of virtually any object or group of small objects wholly or partially enclosed by a volume of space represented by the numerical cell. The core of this scheme is to identify a suitable set of local field solutions to Maxwell's equations within the vicinity of the enclosed objects and, by correctly sampling the fields on the boundary of the cell, to integrate these with field solutions represented by the neighbouring nodes, ensuring both field continuity and power conservation. The idea whilst simple leads to an algorithm that is both explicitly stable and conservative as well as only incurring a minor computational overhead compared to a conventional TLM algorithm. It is noted that, as the required identification and evaluation of the local field solutions occurs as a pre-processing stage prior to the main TLM run and that the non-standard nodes are a small proportion of the coarse grid, a significant overall reduction in computational requirements is achieved in comparison to direct fine meshing of the features. Another advantage of this approach lies in the fact that the local solutions to Maxwell's equations calculated in the pre-run process can be obtained by any suitable means. Analytical formulations, numerical results of another simulation or simply experimental measurements are some of the possibilities. The approach is employed to investigate a variety of EMC problems. An analysis of the field scattered from multiple cylindrical geometries embedded within a single two-dimensional cell is presented. Multiple conducting and lossy wires, dielectric rods and dielectric coated wires, conducting strips and slots are also studied. Three-dimensional simulations are shown for an arbitrarily orientated wires, small dielectric and conducting spheres and other canonical shapes. The approach is also successfully applied to other disciplines where modelling plays an important role. The flexibility of the algorithm is demonstrated for simulations of photonic structures with the primary focus placed upon photonic band-gap materials.

Recent years have seen unprecedented growth in the popularity and deployment of mobile phones. As this continues, so the strain on existing mobile cellular radio network has also increased, leading to the need to investigate new technologies to relieve this pressure. The problem is being further exacerbated by the introduction of the 3rd generation of mobile communications, otherwise known as UMTS (Universal Mobile Telecommunications System), with the aim of offering multimedia services on pocket sized portable receivers. A major cost of the mobile radio network, in terms of both financial and social/environmental aspects, is the need apparent need for more base transceiver stations (BTS), due to the increased number of services, and the density of them. Therefore, judicious use of fewer, but more "intelligent" base stations, thereby reducing the overall system costs, and extra flexibility in the design of mobile cells would be desirable. This can be achieved by having the BTS antennas remotely positioned from the BTS by transmitting the radio signals down an optical fibre or, as in this project, over free space. The main application for this is in densely urban heavy use areas, where there is extensive reuse of both cell and cell cluster. This, along with building shadowing, would require a BTS on every corner, and where extra cell design flexibility would be desirable. Also, in remote rural areas, where various natural features, such as rivers or mountains can cause similar cell design problems, there is a need for this flexibility. The problem with this requirement is that the electrical to optical conversion process, involving a laser diode driver unit, is inherently non-linear, and, unless this is resolved, the desired signal will become unusable due to distortion. To overcome these nonlinearities, a novel correction may be used, based on an optical feedforward correction technique. The prototype system employs off-the-shelf components, and has one Fabry Perot laser diode (FP-LD) providing two signals (via a beam splitter), for a main path and one for the error path loop. The error path signal is detected by a receiver circuit, then mixed with a reference signal to produce a 'pure' error signal, which then modulates the second FP-LD. In contrast with previous fibre feedforward systems, where the two LD outputs are then combined in the optical fibre pre-reception, this system has to combine the signals post-reception. After the main signal and error signal are received and recombined, the non-linearities of the main path are predominantly cancelled by those present in the error path signal, leaving only the desired signal, free of non-linearities.

Although there are numerous single-channel noise reduction strategies to improve speech perception in a noisy environment, most of them can only improve speech quality but not improve speech intelligibility for normal hearing (NH) or hearing impaired (HI) listeners. Exceptions that can improve speech intelligibility currently are only those that require a priori statistics of speech or noise. Most of the noise reduction algorithms in hearing aids are adopted directly from the algorithms for NH listeners without taking into account of the hearing loss factors within HI listeners. HI listeners suffer more in speech intelligibility than NH listeners in the same noisy environment. Further study of monaural noise reduction algorithms for HI listeners is required. The motivation is to adapt a model-based approach in contrast to the conventional Wiener filtering approach. The model-based algorithm called sparse coding shrinkage (SCS) was proposed to extract key speech information from noisy speech. The SCS algorithm was evaluated by comparison with another state-of-the-art Wiener filtering approach through speech intelligibility and quality tests using 9 NH and 9 HI listeners. The SCS algorithm matched the performance of the Wiener filtering algorithm in speech intelligibility and speech quality. Both algorithms showed some intelligibility improvements for HI listeners but not at all for NH listeners. The algorithms improved speech quality for both HI and NH listeners. Additionally, a physiologically-inspired hearing loss simulation (HLS) model was developed to characterize hearing loss factors and simulate hearing loss consequences. A methodology was proposed to evaluate signal processing strategies for HI listeners with the proposed HLS model and NH subjects. The corresponding experiment was performed by asking NH subjects to listen to unprocessed/enhanced speech with the HLS model. Some of the effects of the algorithms seen in HI listeners are reproduced, at least qualitatively, by using the HLS model with NH listeners. Conclusions: The model-based algorithm SCS is promising for improving performance in stationary noise although no clear difference was seen in the performance of SCS and a competitive Wiener filtering algorithm. Fluctuating noise is more difficult to reduce compared to stationary noise. Noise reduction algorithms may perform better at higher input signal-to-noise ratios (SNRs) where HI listeners can get benefit but where NH listeners already reach ceiling performance. The proposed HLS model can save time and cost when evaluating noise reduction algorithms for HI listeners.