Virtual Reality is a powerful tool for training, simulation, and computer aided design. The sensation of being in a real environment, while interacting with VR simulation is usually referred as sense of presence or sense of immersion. In most of the current applications the focus has been in providing a good visual and sound feedback to the user. However, the lack of mechanoreception/touch and proprioception/force feedback, or in other words, the impossibility of really touching the virtual objects makes the interaction unreal and more difficult. The rapid advances in VR and the development of techniques such as virtual medicine, virtual training and virtual prototyping have highlighted the lack of an effective input/feedback interface in these technologies and this led to research activity in all aspects of input and feedback technology related to touch/force sensation and reflection. This work presents the development of a generic integrated haptic (touch/force) feedback interface for use in VR and telepresence applications. The interface presented here consists of a 7 DOF input control/force feedback exoskeleton and a multi-functional input control/touch feedback glove interface. The arm exoskeleton monitors the motions of the human arm and feed back force sensations using ultra light weight pneumatic Muscle Actuators (pMA) to obtain high power weight outputs in a light comfortable and inherently safe structure. The glove interface monitors the motions of the hand and feed back touch sensations such as contact pressure, surface texture and temperature. These hardware systems have been integrated together and have been interfaced with a virtual reality system to permit exploration and testing of interactions with virtual environments. It is believed that the use of the system in VR, particularly, in all design and rapid prototyping applications will provide enhanced performance and will augment the design-production process.

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Recently, the more widespread use of three-dimensional (3-D) imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and ultrasound (US) have generated a massive amount of volumetric data. These have provided an impetus to the development of other applications, in particular telemedicine and teleradiology. In these fields, medical image compression is important since both efficient storage and transmission of data through high-bandwidth digital communication lines are of crucial importance. Despite their advantages, most 3-D medical imaging algorithms are computationally intensive with matrix transformation as the most fundamental operation involved in the transform-based methods. Therefore, there is a real need for high-performance systems, whilst keeping architectures exible to allow for quick upgradeability with real-time applications. Moreover, in order to obtain efficient solutions for large medical volumes data, an efficient implementation of these operations is of significant importance. Reconfigurable hardware, in the form of field programmable gate arrays (FPGAs) has been proposed as viable system building block in the construction of high-performance systems at an economical price. Consequently, FPGAs seem an ideal candidate to harness and exploit their inherent advantages such as massive parallelism capabilities, multimillion gate counts, and special low-power packages. The key achievements of the work presented in this thesis are summarised as follows. Two architectures for 3-D Haar wavelet transform (HWT) have been proposed based on transpose-based computation and partial reconfiguration suitable for 3-D medical imaging applications. These applications require continuous hardware servicing, and as a result dynamic partial reconfiguration (DPR) has been introduced. Comparative study for both non-partial and partial reconfiguration implementation has shown that DPR offers many advantages and leads to a compelling solution for implementing computationally intensive applications such as 3-D medical image compression. Using DPR, several large systems are mapped to small hardware resources, and the area, power consumption as well as maximum frequency are optimised and improved. Moreover, an FPGA-based architecture of the finite Radon transform (FRAT)with three design strategies has been proposed: direct implementation of pseudo-code with a sequential or pipelined description, and block random access memory (BRAM)- based method. An analysis with various medical imaging modalities has been carried out. Results obtained for image de-noising implementation using FRAT exhibits promising results in reducing Gaussian white noise in medical images. In terms of hardware implementation, promising trade-offs on maximum frequency, throughput and area are also achieved. Furthermore, a novel hardware implementation of 3-D medical image compression system with context-based adaptive variable length coding (CAVLC) has been proposed. An evaluation of the 3-D integer transform (IT) and the discrete wavelet transform (DWT) with lifting scheme (LS) for transform blocks reveal that 3-D IT demonstrates better computational complexity than the 3-D DWT, whilst the 3-D DWT with LS exhibits a lossless compression that is significantly useful for medical image compression. Additionally, an architecture of CAVLC that is capable of compressing high-definition (HD) images in real-time without any buffer between the quantiser and the entropy coder is proposed. Through a judicious parallelisation, promising results have been obtained with limited resources. In summary, this research is tackling the issues of massive 3-D medical volumes data that requires compression as well as hardware implementation to accelerate the slowest operations in the system. Results obtained also reveal a significant achievement in terms of the architecture efficiency and applications performance.

The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy.

The Scale Invariant Feature Transform (SIFT) is one of the most popular matching algorithms in the field of computer vision. It takes over many other algorithms because features detected are fully invariant to image scaling and rotation, and are also shown to be robust to changes in 3D viewpoint, addition of noise, changes in illumination and a sustainable range of affine distortion. However, the computational complexity is high, which prevents it from achieving real-time. The aim of this project, therefore, is to develop a high-performance image matching system based on the optimised SIFT algorithm to perform real-time feature detection, description and matching. This thesis presents the stages of the development of the system. To reduce the computational complexity, an alternative to the grid layout of standard SIFT is proposed, which is termed as SRI-DASIY (Scale and Rotation Invariant DAISY). The SRI-DAISY achieves comparable performance with the standard SIFT descriptor, but is more efficient to be implemented using hardware, in terms of both computational complexity and memory usage. The design takes only 7.57 µs to generate a descriptor with a system frequency of 100 MHz, which is equivalent to approximately 132,100 descriptors per second and is of the highest throughput when compared with existing designs. Besides, a novel keypoint matching strategy is also presented in this thesis, which achieves higher precision than the widely applied distance ratio based matching and is computationally more efficient. All phases of the SIFT algorithm have been investigated, including feature detection, descriptor generation and descriptor matching. The characterisation of each individual part of the design is carried out and compared with the software simulation results. A fully stand-alone image matching system has been developed that consists of a CMOS camera front-end for image capture, a SIFT processing core embedded in a Field Programmable Logic Array (FPGA) device, and a USB back-end for data transfer. Experiments are conducted by using real-world images to verify the system performance. The system has been tested by integrating into two practical applications. The resulting image matching system eliminates the bottlenecks that limit the overall throughput of the system, and hence allowing the system to process images in real-time without interruption. The design can be modified to adapt to the applications processing images with higher resolution and is still able to achieve real-time.

Wireless Communication is the only solution for data transfer between mobile terminals to access the sensors and actuators in industrial environment Control Area Network (CAN) is desirable solution for many industrial applications since it meets the requirements of real-time transfer of messages between systems. In situations where the use of a cable is not feasible it is important and necessary to design wireless medium access control protocols for CAN to provide real-time communications. This thesis deals with modelling, simulation and performance analysis of wireless medium access control protocols for CAN. The main issue in this concept is to determine prioritisation of the messages in the wireless environment. In order to accomplish this, a Wireless Medium Access Control protocol called WMAC is first proposed for distributed environment. The prioritisation in the WMAC protocol is achieved by performing an operation of timing the interframe gap. In this method, every message within the network is assigned a unique time period before the transmission of the message. These individual time periods distinguish messages from each other and provides message priority. Second access method called Remote Frame Medium Access Control (RFMAC) protocol is proposed for centralised wireless environment. Since the central node organises the message traffic the prioritisation is accomplished automatically by the central node. Both protocols are evaluated by using simulation techniques. The third access method called Comb is designed by using an additional overhead which consist of binary sequence. The prioritisation in this access method is managed by the overhead. Additionally, the interconnection of wireless nodes is investigated. The results of the simulations and performance analysis show that the proposed protocols operating in the centralised and distributed environments are capable of supporting the prioritisation of the messages required for real-time industrial applications in a wireless environment.

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