High Speed Optical Links Using CAP Modulation and Novel Equalisation Techniques

High speed optical links suffer from inter-symbol-interference (ISI) due to their limited bandwidth. Equalisation is typically used to mitigate ISI and therefore improve the link capacity. This dissertation explores novel equalisation techniques for carrierless amplitude and phase (CAP) modulation based optical communication systems including OM4 based and plastic optical fibre (POF) based links. An 850 nm VCSEL based OM4 link using CAP-16 scheme is studied. For the first time, the CAP equaliser, is proposed to mitigate both crosstalk channel interference (CCI) and ISI in the link at the receiver side. Performance comparisons are studied between the CAP-16 scheme using CAP equaliser and a conventional equaliser, pulse amplitude modulation (PAM-4) scheme, and discrete multitone (DMT) scheme. CAP based data transmission of 112 Gb/s is achieved over 150 m OM4 fibre with this novel equaliser, while the conventional equaliser can only support over 1 m OM4 fibre and fails to recover the signals at the same data rate. In addition, this novel equaliser provides a 1.2 dB and 1.7 dB improvement in receiver sensitivity over PAM-4 and DMT schemes, respectively, at 112 Gb/s over 100 m OM4 fibre. A novel pre-CAP-equaliser solving CCI at the transmitter side is also proposed. Data transmission of 56 Gb/s over 100 m OM4 fibre is reported experimentally with an improvement of 0.7 dB in receiver sensitivity compared to using the CAP equaliser at the receiver side. A simulation study shows a 2 dB improvement in receiver sensitivity at 112 Gb/s over 100 m OM4 fibre. Furthermore, an artificial neural network (ANN) equaliser in conjunction with the CAP equaliser structure is explored in a VCSEL based OM4 fibre link in order to further mitigate the nonlinear impairments. For 112 Gb/s data transmission over 100 m OM4 fibre, a 2.4 dB improvement of receiver sensitivity is achieved compared to the CAP equaliser. In addition to the electrical equalisers, a monolithically integrated silicon optical equaliser consisting of three taps is used for 50 Gb/s data transmission. After 10 km standard single mode fibre (SSMF), error free eye diagrams at the receiver are demonstrated. A μLED based POF link based on an APD receiver is also investigated with the CAP equaliser at the receiver side. Data transmission rates of 4 Gb/s over 25 m and 5 Gb/s over 10 m POF links are demonstrated with this equaliser while the conventional equaliser can only support 4 Gb/s over 10 m and fails to recover the signals for 5 Gb/s data transmission

The Comprehensive Review of Neural Network: An Intelligent Medical Image Compression for Data Sharing

In the healthcare environment, digital images are the most commonly shared information. It has become a vital resource in health care services that facilitates decision-making and treatment procedures. The medical image requires large volumes of storage and the storage scale continues to grow because of the advancement of medical image technology. To enhance the interaction and coordination between healthcare institutions, the efficient exchange of medical information is necessary. Therefore, the sharing of the medical image with zero loss of information and efficiency needs to be guaranteed exactly. Image compression helps ensure that the purpose of sharing this data from a medical image must be as intelligent as possible to contain valuable information while at the same time minimizing unnecessary diagnostic information. Artificial Neural Network has been used to solve many issues in the processing of images. It has proved its dominance in the handling of noisy or incomplete image compression applications over traditional methods. It contributes to the resulting image by a high compression ratio and noise reduction. This paper reviews previous studies on the compression of intelligent medical images with the neural network approach to data sharing

Voltage equalisation techniques for high capacitance device modules

Phd ThesisTraditionally, the electrochemical battery has been the prime medium by which electrical energy is stored for future use. Increasingly, the demands of modern systems such as electric vehicles, renewable energy, distributed generation, smart grid and others has stretched the development of new chemistries, materials and assembly techniques for electrochemical batteries. Additionally, some load profiles in these applications demand extremely high dynamic behaviour which is either undeliverable by conventional electrochemical batteries or is undesirably damaging to these technologies. As such, a family of electrochemical storage, known generally as supercapacitors or ultracapacitors, have been developed and implemented for such applications. In recent years advancements in electrochemical technology has led to hybridisation of high capacitance devices. Lithium-ion capacitors that are used in this work are, with their higher cell voltage and modern packaging, expected to be among the next emerging families of state-of-the-art electrical energy storage devices. The relatively low cell voltage of high capacitance cells requires them to be connected in series to attain a system level voltage. During charging and discharging, manufacturing tolerances between the cells results in voltage mismatch across the stack. Mismatched voltages are an inefficient use of the energy storage medium and can lead to dangerous failures in the cells. Several techniques exist to limit the variance in cell voltages of supercapacitors across a series connected stack. These range from simple systems which discharge the cells at higher voltages through resistors to more complex active converter systems which equalise the cell voltages through charge redistribution via a power electronic converter. Whilst the simpler schemes are effective they are very inefficient and as such are not suitable for use in many applications. A number of active converter voltage equalisation schemes have been proposed in literature, however, each of these equalisation schemes exhibit flaws which either makes them less desirable or less effective for a broad range of applications. Therefore, a new equalisation converter topology is proposed which is designed for greater equalisation effectiveness, modularity and size. The proposed equalisation converter differs from previously published equalisation schemes by allowing energy transfer between any pair of cells without the cumbersome multi-winding transformers employed in existing equalisation converters. The new equalisation scheme uses a bi-directional arrangement of MOSFET switches for galvanostatic isolation allowing the converter to be multiplexed to the stack. This arrangement allows the total size of the equalisation scheme to be reduced whilst maintaining performance.EPSRC

Video Processing Acceleration using Reconfigurable Logic and Graphics Processors

A vexing question is `which architecture will prevail as the core feature of the next state of the art video processing system?' This thesis examines the substitutive and collaborative use of the two alternatives of the reconfigurable logic and graphics processor architectures. A structured approach to executing architecture comparison is presented - this includes a proposed `Three Axes of Algorithm Characterisation' scheme and a formulation of perfor- mance drivers. The approach is an appealing platform for clearly defining the problem, assumptions and results of a comparison. In this work it is used to resolve the advanta- geous factors of the graphics processor and reconfigurable logic for video processing, and the conditions determining which one is superior. The comparison results prompt the exploration of the customisable options for the graphics processor architecture. To clearly define the architectural design space, the graphics processor is first identifed as part of a wider scope of homogeneous multi-processing element (HoMPE) architectures. A novel exploration tool is described which is suited to the investigation of the customisable op- tions of HoMPE architectures. The tool adopts a systematic exploration approach and a high-level parameterisable system model, and is used to explore pre- and post-fabrication customisable options for the graphics processor. A positive result of the exploration is the proposal of a reconfigurable engine for data access (REDA) to optimise graphics processor performance for video processing-specific memory access patterns. REDA demonstrates the viability of the use of reconfigurable logic as collaborative `glue logic' in the graphics processor architecture

Artificial Neural Network Based Channel Equalization

The field of digital data communications has experienced an explosive growth in the last three decade with the growth of internet technologies, high speed and efficient data transmission over communication channel has gained significant importance. The rate of data transmissions over a communication system is limited due to the effects of linear and nonlinear distortion. Linear distortions occure in from of inter-symbol interference (ISI), co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise. Nonlinear distortions are caused due to the subsystems like amplifiers, modulator and demodulator along with nature of the medium. Some times burst noise occurs in communication system. Different equalization techniques are used to mitigate these effects. Adaptive channel equalizers are used in digital communication systems. The equalizer located at the receiver removes the effects of ISI, CCI, burst noise interference and attempts to recover the transmitted symbols. It has been seen that linear equalizers show poor performance, where as nonlinear equalizer provide superior performance. Artificial neural network based multi layer perceptron (MLP) based equalizers have been used for equalization in the last two decade. The equalizer is a feed-forward network consists of one or more hidden nodes between its input and output layers and is trained by popular error based back propagation (BP) algorithm. However this algorithm suffers from slow convergence rate, depending on the size of network. It has been seen that an optimal equalizer based on maximum a-posterior probability (MAP) criterion can be implemented using Radial basis function (RBF) network. In a RBF equalizer, centres are fixed using K-mean clustering and weights are trained using LMS algorithm. RBF equalizer can mitigate ISI interference effectively providing minimum BER plot. But when the input order is increased the number of centre of the network increases and makes the network more complicated. A RBF network, to mitigate the effects of CCI is very complex with large number of centres. To overcome computational complexity issues, a single neuron based chebyshev neural network (ChNN) and functional link ANN (FLANN) have been proposed. These neural networks are single layer network in which the original input pattern is expanded to a higher dimensional space using nonlinear functions and have capability to provide arbitrarily complex decision regions. More recently, a rank based statistics approach known as Wilcoxon learning method has been proposed for signal processing application. The Wilcoxon learning algorithm has been applied to neural networks like Wilcoxon Multilayer Perceptron Neural Network (WMLPNN), Wilcoxon Generalized Radial Basis Function Network (WGRBF). The Wilcoxon approach provides promising methodology for many machine learning problems. This motivated us to introduce these networks in the field of channel equalization application. In this thesis we have used WMLPNN and WGRBF network to mitigate ISI, CCI and burst noise interference. It is observed that the equalizers trained with Wilcoxon learning algorithm offers improved performance in terms of convergence characteristic and bit error rate performance in comparison to gradient based training for MLP and RBF. Extensive simulation studies have been carried out to validate the proposed technique. The performance of Wilcoxon networks is better then linear equalizers trained with LMS and RLS algorithm and RBF equalizer in the case of burst noise and CCI mitigations

Multimodal enhancement-fusion technique for natural images.

Masters Degree. University of KwaZulu-Natal, Durban.This dissertation presents a multimodal enhancement-fusion (MEF) technique for natural images. The MEF is expected to contribute value to machine vision applications and personal image collections for the human user. Image enhancement techniques and the metrics that are used to assess their performance are prolific, and each is usually optimised for a specific objective. The MEF proposes a framework that adaptively fuses multiple enhancement objectives into a seamless pipeline. Given a segmented input image and a set of enhancement methods, the MEF applies all the enhancers to the image in parallel. The most appropriate enhancement in each image segment is identified, and finally, the differentially enhanced segments are seamlessly fused. To begin with, this dissertation studies targeted contrast enhancement methods and performance metrics that can be utilised in the proposed MEF. It addresses a selection of objective assessment metrics for contrast-enhanced images and determines their relationship with the subjective assessment of human visual systems. This is to identify which objective metrics best approximate human assessment and may therefore be used as an effective replacement for tedious human assessment surveys. A subsequent human visual assessment survey is conducted on the same dataset to ascertain image quality as perceived by a human observer. The interrelated concepts of naturalness and detail were found to be key motivators of human visual assessment. Findings show that when assessing the quality or accuracy of these methods, no single quantitative metric correlates well with human perception of naturalness and detail, however, a combination of two or more metrics may be used to approximate the complex human visual response. Thereafter, this dissertation proposes the multimodal enhancer that adaptively selects the optimal enhancer for each image segment. MEF focusses on improving chromatic irregularities such as poor contrast distribution. It deploys a concurrent enhancement pathway that subjects an image to multiple image enhancers in parallel, followed by a fusion algorithm that creates a composite image that combines the strengths of each enhancement path. The study develops a framework for parallel image enhancement, followed by parallel image assessment and selection, leading to final merging of selected regions from the enhanced set. The output combines desirable attributes from each enhancement pathway to produce a result that is superior to each path taken alone. The study showed that the proposed MEF technique performs well for most image types. MEF is subjectively favourable to a human panel and achieves better performance for objective image quality assessment compared to other enhancement methods

A Combined Equaliser and Decoder for Maximum Likelihood Decoding of Convolutional Codes in the presence of ISI. Incorporation into GSM 3GPP Standard

The dissertation describes a new approach in combining the equalising and decoding operations in wireless telecommunications, namely MS decoder. It provides performance results (SNR) and carries out simulations based on GSM 3GPP standard

Physical and Link Layer Implications in Vehicle Ad Hoc Networks

Vehicle Ad hoc Networks (V ANET) have been proposed to provide safety on the road and deliver road traffic information and route guidance to drivers along with commercial applications. However the challenges facing V ANET are numerous. Nodes move at high speeds, road side units and basestations are scarce, the topology is constrained by the road geometry and changes rapidly, and the number of nodes peaks suddenly in traffic jams. In this thesis we investigate the physical and link layers of V ANET and propose methods to achieve high data rates and high throughput. For the physical layer, we examine the use of Vertical BLAST (VB LAST) systems as they provide higher capacities than single antenna systems in rich fading environments. To study the applicability of VB LAST to VANET, a channel model was developed and verified using measurement data available in the literature. For no to medium line of sight, VBLAST systems provide high data rates. However the performance drops as the line of sight strength increases due to the correlation between the antennas. Moreover, the performance of VBLAST with training based channel estimation drops as the speed increases since the channel response changes rapidly. To update the channel state information matrix at the receiver, a channel tracking algorithm for flat fading channels was developed. The algorithm updates the channel matrix thus reducing the mean square error of the estimation and improving the bit error rate (BER). The analysis of VBLAST-OFDM systems showed they experience an error floor due to inter-carrier interference (lCI) which increases with speed, number of antennas transmitting and number of subcarriers used. The update algorithm was extended to VBLAST -OFDM systems and it showed improvements in BER performance but still experienced an error floor. An algorithm to equalise the ICI contribution of adjacent subcarriers was then developed and evaluated. The ICI equalisation algorithm reduces the error floor in BER as more subcarriers are equalised at the expense of more hardware complexity. The connectivity of V ANET was investigated and it was found that for single lane roads, car densities of 7 cars per communication range are sufficient to achieve high connectivity within the city whereas 12 cars per communication range are required for highways. Multilane roads require higher densities since cars tend to cluster in groups. Junctions and turns have lower connectivity than straight roads due to disconnections at the turns. Although higher densities improve the connectivity and, hence, the performance of the network layer, it leads to poor performance at the link layer. The IEEE 802.11 p MAC layer standard under development for V ANET uses a variant of Carrier Sense Multiple Access (CSMA). 802.11 protocols were analysed mathematically and via simulations and the results prove the saturation throughput of the basic access method drops as the number of nodes increases thus yielding very low throughput in congested areas. RTS/CTS access provides higher throughput but it applies only to unicast transmissions. To overcome the limitations of 802.11 protocols, we designed a protocol known as SOFT MAC which combines Space, Orthogonal Frequency and Time multiple access techniques. In SOFT MAC the road is divided into cells and each cell is allocated a unique group of subcarriers. Within a cell, nodes share the available subcarriers using a combination of TDMA and CSMA. The throughput analysis of SOFT MAC showed it has superior throughput compared to the basic access and similar to the RTS/CTS access of 802.11